import random
from collections import OrderedDict
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()
# plt.style.use('seaborn-white')
# plt.style.use('ggplot')
data_reduction = OrderedDict()
SEED = 1337
def seed_everything(seed):
np.random.seed(seed)
random.seed(seed)
seed_everything(SEED)AML Mini-Challenge - Credit Card Affinity Modelling
1 Data Import & Wrangling
1.1 Helper Functions
def remap_values(df, column, mapping):
# assert that all values in the column are in the mapping except for NaN
assert df[column].dropna().isin(mapping.keys()).all()
df[column] = df[column].map(mapping, na_action="ignore")
return df
def map_empty_to_nan(df, column):
if df[column].dtype != "object":
return df
df[column] = df[column].replace(r"^\s*$", np.nan, regex=True)
return df
def read_csv(file_path, sep=";", dtypes=None):
df = pd.read_csv(file_path, sep=sep, dtype=dtypes)
for col in df.columns:
df = map_empty_to_nan(df, col)
return dfdef plot_categorical_variables(df, categorical_columns, fill_na_value="NA"):
"""
Plots count plots for categorical variables in a DataFrame, filling NA values with a specified string.
Parameters:
- df: pandas.DataFrame containing the data.
- categorical_vars: list of strings, names of the categorical variables in df to plot.
- fill_na_value: string, the value to use for filling NA values in the categorical variables.
"""
# Fill NA values in the specified categorical variables
for var in categorical_columns:
if df[var].isna().any():
df[var] = df[var].fillna(fill_na_value)
total = float(len(df))
fig, axes = plt.subplots(
nrows=len(categorical_columns), figsize=(8, len(categorical_columns) * 5)
)
if len(categorical_columns) == 1: # If there's only one categorical variable, wrap axes in a list
axes = [axes]
for i, var in enumerate(categorical_columns):
ax = sns.countplot(
x=var, data=df, ax=axes[i], order=df[var].value_counts().index
)
axes[i].set_title(f"Distribution of {var}")
axes[i].set_ylabel("Count")
axes[i].set_xlabel(var)
# if the number is more thatn 6 rotate the x labels
if len(df[var].value_counts()) > 6:
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
for p in ax.patches:
height = p.get_height()
ax.text(
p.get_x() + p.get_width() / 2.0,
height + 3,
"{:1.2f}%".format((height / total) * 100),
ha="center",
)
plt.tight_layout()
plt.show()
def plot_numerical_distributions(df, numerical_columns, kde=True, bins=30):
"""
Plots the distribution of all numerical variables in a DataFrame.
Parameters:
- df: pandas.DataFrame containing the data.
"""
# Determine the number of rows needed for subplots based on the number of numerical variables
nrows = len(numerical_columns)
# Create subplots
fig, axes = plt.subplots(nrows=nrows, ncols=1, figsize=(8, 5 * nrows))
if nrows == 1: # If there's only one numerical variable, wrap axes in a list
axes = [axes]
for i, var in enumerate(numerical_columns):
sns.histplot(df[var], ax=axes[i], kde=kde, bins=bins)
axes[i].set_title(f"Distribution of {var}")
axes[i].set_xlabel(var)
axes[i].set_ylabel("Frequency")
plt.tight_layout()
plt.show()
def plot_date_monthly_counts(df, date_column, title):
"""
Plots the monthly counts of a date column in a DataFrame.
Parameters:
- df: pandas.DataFrame containing the data.
- date_column: string, name of the date column in df to plot.
- title: string, title of the plot.
"""
df[date_column] = pd.to_datetime(df[date_column])
df["month"] = df[date_column].dt.to_period("M")
monthly_counts = df["month"].value_counts().sort_index()
monthly_counts.plot(kind="bar")
plt.title(title)
plt.xlabel("Month")
plt.ylabel("Count")
plt.show()
def add_percentage_labels(ax, hue_order):
for p in ax.patches:
height = p.get_height()
width = p.get_width()
x = p.get_x()
y = p.get_y()
label_text = f"{height:.1f}%"
label_x = x + width / 2
label_y = y + height / 2
ax.text(
label_x,
label_y,
label_text,
ha="center",
va="center",
fontsize=9,
color="white",
weight="bold"
)1.2 Entities
1.2.1 Accounts
accounts_df = read_csv("data/account.csv")
# Translated frequency from Czech to English
# according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
accounts_df = remap_values(
accounts_df,
"frequency",
{
"POPLATEK MESICNE": "MONTHLY_ISSUANCE",
"POPLATEK TYDNE": "WEEKLY_ISSUANCE",
"POPLATEK PO OBRATU": "ISSUANCE_AFTER_TRANSACTION",
},
)
accounts_df["date"] = pd.to_datetime(accounts_df["date"], format="%y%m%d")
accounts_df.rename(
columns={"date": "account_created", "frequency": "account_frequency"}, inplace=True
)
data_reduction["Total number of accounts"] = len(accounts_df)
accounts_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4500 entries, 0 to 4499
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 account_id 4500 non-null int64
1 district_id 4500 non-null int64
2 account_frequency 4500 non-null object
3 account_created 4500 non-null datetime64[ns]
dtypes: datetime64[ns](1), int64(2), object(1)
memory usage: 140.8+ KB
# todo add some basic eda here
accounts_df.head()| account_id | district_id | account_frequency | account_created | |
|---|---|---|---|---|
| 0 | 576 | 55 | MONTHLY_ISSUANCE | 1993-01-01 |
| 1 | 3818 | 74 | MONTHLY_ISSUANCE | 1993-01-01 |
| 2 | 704 | 55 | MONTHLY_ISSUANCE | 1993-01-01 |
| 3 | 2378 | 16 | MONTHLY_ISSUANCE | 1993-01-01 |
| 4 | 2632 | 24 | MONTHLY_ISSUANCE | 1993-01-02 |
accounts_df.nunique()account_id 4500
district_id 77
account_frequency 3
account_created 1535
dtype: int64
plot_categorical_variables(accounts_df, ["account_frequency"])plot_numerical_distributions(accounts_df, ["account_created"])1.2.2 Clients
clients_df = read_csv("data/client.csv")
def parse_birth_number(birth_number):
birth_number_str = str(birth_number)
# Extract year, month, and day from birth number from string
# according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
year = int(birth_number_str[:2])
month = int(birth_number_str[2:4])
day = int(birth_number_str[4:6])
# Determine sex based on month and adjust month for female clients
# according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
if month > 50:
sex = "Female"
month -= 50
else:
sex = "Male"
# Validate date
assert 1 <= month <= 12
assert 1 <= day <= 31
assert 0 <= year <= 99
if month in [4, 6, 9, 11]:
assert 1 <= day <= 30
elif month == 2:
assert 1 <= day <= 29
else:
assert 1 <= day <= 31
# Assuming all dates are in the 1900s
birth_date = datetime(1900 + year, month, day)
return pd.Series([sex, birth_date])
clients_df[["sex", "birth_date"]] = clients_df["birth_number"].apply(parse_birth_number)
# Calculate 'age' assuming the reference year is 1999
clients_df["age"] = clients_df["birth_date"].apply(lambda x: 1999 - x.year)
# Drop 'birth_number' column as it is no longer needed
clients_df = clients_df.drop(columns=["birth_number"])
clients_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5369 entries, 0 to 5368
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 client_id 5369 non-null int64
1 district_id 5369 non-null int64
2 sex 5369 non-null object
3 birth_date 5369 non-null datetime64[ns]
4 age 5369 non-null int64
dtypes: datetime64[ns](1), int64(3), object(1)
memory usage: 209.9+ KB
# todo add some basic eda here
clients_df.head()| client_id | district_id | sex | birth_date | age | |
|---|---|---|---|---|---|
| 0 | 1 | 18 | Female | 1970-12-13 | 29 |
| 1 | 2 | 1 | Male | 1945-02-04 | 54 |
| 2 | 3 | 1 | Female | 1940-10-09 | 59 |
| 3 | 4 | 5 | Male | 1956-12-01 | 43 |
| 4 | 5 | 5 | Female | 1960-07-03 | 39 |
clients_df.describe()| client_id | district_id | birth_date | age | |
|---|---|---|---|---|
| count | 5369.000000 | 5369.000000 | 5369 | 5369.000000 |
| mean | 3359.011920 | 37.310114 | 1953-09-12 09:32:21.143602176 | 45.801639 |
| min | 1.000000 | 1.000000 | 1911-08-20 00:00:00 | 12.000000 |
| 25% | 1418.000000 | 14.000000 | 1940-11-25 00:00:00 | 31.000000 |
| 50% | 2839.000000 | 38.000000 | 1954-05-06 00:00:00 | 45.000000 |
| 75% | 4257.000000 | 60.000000 | 1968-06-09 00:00:00 | 59.000000 |
| max | 13998.000000 | 77.000000 | 1987-09-27 00:00:00 | 88.000000 |
| std | 2832.911984 | 25.043690 | NaN | 17.282283 |
plot_numerical_distributions(clients_df, ["birth_date", "age"])1.2.3 Dispositions
dispositions_df = read_csv("data/disp.csv")
dispositions_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5369 entries, 0 to 5368
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 disp_id 5369 non-null int64
1 client_id 5369 non-null int64
2 account_id 5369 non-null int64
3 type 5369 non-null object
dtypes: int64(3), object(1)
memory usage: 167.9+ KB
dispositions_df.head()| disp_id | client_id | account_id | type | |
|---|---|---|---|---|
| 0 | 1 | 1 | 1 | OWNER |
| 1 | 2 | 2 | 2 | OWNER |
| 2 | 3 | 3 | 2 | DISPONENT |
| 3 | 4 | 4 | 3 | OWNER |
| 4 | 5 | 5 | 3 | DISPONENT |
dispositions_df.describe()| disp_id | client_id | account_id | |
|---|---|---|---|
| count | 5369.000000 | 5369.000000 | 5369.000000 |
| mean | 3337.097970 | 3359.011920 | 2767.496927 |
| std | 2770.418826 | 2832.911984 | 2307.843630 |
| min | 1.000000 | 1.000000 | 1.000000 |
| 25% | 1418.000000 | 1418.000000 | 1178.000000 |
| 50% | 2839.000000 | 2839.000000 | 2349.000000 |
| 75% | 4257.000000 | 4257.000000 | 3526.000000 |
| max | 13690.000000 | 13998.000000 | 11382.000000 |
plot_categorical_variables(dispositions_df, ["type"])As the goal of this model is to address accounts and not client directly we will focus on the clients which own an account and focus solely on them.
dispositions_df = dispositions_df[dispositions_df["type"] == "OWNER"]1.2.4 Orders
orders_df = read_csv("data/order.csv")
# Translated from Czech to English
# according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
orders_df = remap_values(
orders_df,
"k_symbol",
{
"POJISTNE": "Insurance_Payment",
"SIPO": "Household",
"LEASING": "Leasing",
"UVER": "Loan_Payment",
},
)
orders_df["account_to"] = orders_df["account_to"].astype("category")
orders_df = orders_df.rename(columns={"amount": "debited_amount"})
orders_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6471 entries, 0 to 6470
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 order_id 6471 non-null int64
1 account_id 6471 non-null int64
2 bank_to 6471 non-null object
3 account_to 6471 non-null category
4 debited_amount 6471 non-null float64
5 k_symbol 5092 non-null object
dtypes: category(1), float64(1), int64(2), object(2)
memory usage: 573.9+ KB
orders_df.head()| order_id | account_id | bank_to | account_to | debited_amount | k_symbol | |
|---|---|---|---|---|---|---|
| 0 | 29401 | 1 | YZ | 87144583 | 2452.0 | Household |
| 1 | 29402 | 2 | ST | 89597016 | 3372.7 | Loan_Payment |
| 2 | 29403 | 2 | QR | 13943797 | 7266.0 | Household |
| 3 | 29404 | 3 | WX | 83084338 | 1135.0 | Household |
| 4 | 29405 | 3 | CD | 24485939 | 327.0 | NaN |
orders_df.describe()| order_id | account_id | debited_amount | |
|---|---|---|---|
| count | 6471.000000 | 6471.000000 | 6471.000000 |
| mean | 33778.197497 | 2962.302890 | 3280.635698 |
| std | 3737.681949 | 2518.503228 | 2714.475335 |
| min | 29401.000000 | 1.000000 | 1.000000 |
| 25% | 31187.500000 | 1223.000000 | 1241.500000 |
| 50% | 32988.000000 | 2433.000000 | 2596.000000 |
| 75% | 34785.500000 | 3645.500000 | 4613.500000 |
| max | 46338.000000 | 11362.000000 | 14882.000000 |
orders_df.nunique()order_id 6471
account_id 3758
bank_to 13
account_to 6446
debited_amount 4412
k_symbol 4
dtype: int64
There appear to be as many order ids as there are rows.
plot_categorical_variables(orders_df, ["k_symbol", "bank_to"])/tmp/ipykernel_1879/945940023.py:33: UserWarning:
set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
plot_numerical_distributions(orders_df, ["debited_amount"])1.2.5 Transactions
# column 8 is the 'bank' column which contains NaNs and must be read as string
transactions_df = read_csv("data/trans.csv", dtypes={8: str})
transactions_df["date"] = pd.to_datetime(transactions_df["date"], format="%y%m%d")
# Translated type, operations and characteristics from Czech to English
# according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
transactions_df = remap_values(
transactions_df,
"type",
{
"VYBER": "Withdrawal", # Also withdrawal as it is against the documentation present in the dataset
"PRIJEM": "Credit",
"VYDAJ": "Withdrawal",
},
)
transactions_df = remap_values(
transactions_df,
"operation",
{
"VYBER KARTOU": "Credit Card Withdrawal",
"VKLAD": "Credit in Cash",
"PREVOD Z UCTU": "Collection from Another Bank",
"VYBER": "Withdrawal in Cash",
"PREVOD NA UCET": "Remittance to Another Bank",
},
)
transactions_df = remap_values(
transactions_df,
"k_symbol",
{
"POJISTNE": "Insurance Payment",
"SLUZBY": "Payment on Statement",
"UROK": "Interest Credited",
"SANKC. UROK": "Sanction Interest",
"SIPO": "Household",
"DUCHOD": "Old-age Pension",
"UVER": "Loan Payment",
},
)
# Set the amount to negative for withdrawals and positive for credits
transactions_df["amount"] = np.where(
transactions_df["type"] == "Credit",
transactions_df["amount"],
-transactions_df["amount"],
)
transactions_df.rename(columns={"type": "transaction_type"}, inplace=True)
transactions_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1056320 entries, 0 to 1056319
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 trans_id 1056320 non-null int64
1 account_id 1056320 non-null int64
2 date 1056320 non-null datetime64[ns]
3 transaction_type 1056320 non-null object
4 operation 873206 non-null object
5 amount 1056320 non-null float64
6 balance 1056320 non-null float64
7 k_symbol 521006 non-null object
8 bank 273508 non-null object
9 account 295389 non-null float64
dtypes: datetime64[ns](1), float64(3), int64(2), object(4)
memory usage: 80.6+ MB
transactions_df.head()| trans_id | account_id | date | transaction_type | operation | amount | balance | k_symbol | bank | account | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 695247 | 2378 | 1993-01-01 | Credit | Credit in Cash | 700.0 | 700.0 | NaN | NaN | NaN |
| 1 | 171812 | 576 | 1993-01-01 | Credit | Credit in Cash | 900.0 | 900.0 | NaN | NaN | NaN |
| 2 | 207264 | 704 | 1993-01-01 | Credit | Credit in Cash | 1000.0 | 1000.0 | NaN | NaN | NaN |
| 3 | 1117247 | 3818 | 1993-01-01 | Credit | Credit in Cash | 600.0 | 600.0 | NaN | NaN | NaN |
| 4 | 579373 | 1972 | 1993-01-02 | Credit | Credit in Cash | 400.0 | 400.0 | NaN | NaN | NaN |
transactions_df.describe()| trans_id | account_id | date | amount | balance | account | |
|---|---|---|---|---|---|---|
| count | 1.056320e+06 | 1.056320e+06 | 1056320 | 1.056320e+06 | 1.056320e+06 | 2.953890e+05 |
| mean | 1.335311e+06 | 2.936867e+03 | 1997-01-04 07:29:27.037261952 | 1.866397e+02 | 3.851833e+04 | 4.567092e+07 |
| min | 1.000000e+00 | 1.000000e+00 | 1993-01-01 00:00:00 | -8.740000e+04 | -4.112570e+04 | 0.000000e+00 |
| 25% | 4.302628e+05 | 1.204000e+03 | 1996-01-16 00:00:00 | -3.019000e+03 | 2.240250e+04 | 1.782858e+07 |
| 50% | 8.585065e+05 | 2.434000e+03 | 1997-04-10 00:00:00 | -1.460000e+01 | 3.314340e+04 | 4.575095e+07 |
| 75% | 2.060979e+06 | 3.660000e+03 | 1998-02-28 00:00:00 | 2.000000e+02 | 4.960362e+04 | 7.201341e+07 |
| max | 3.682987e+06 | 1.138200e+04 | 1998-12-31 00:00:00 | 7.481200e+04 | 2.096370e+05 | 9.999420e+07 |
| std | 1.227487e+06 | 2.477345e+03 | NaN | 1.121353e+04 | 2.211787e+04 | 3.066340e+07 |
plot_categorical_variables(
transactions_df, ["transaction_type", "operation", "k_symbol"]
)/tmp/ipykernel_1879/945940023.py:33: UserWarning:
set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
plot_numerical_distributions(transactions_df, ["date", "amount", "balance"])Looking at the distributions of the transaction table we can see that the count of transactions per year increase over time. So we can conclude that the bank has a growing client base.
However, the other plots are not very useful. For one the transaction amount seems to be very sparse, ranging from values between -80000 and 80000.
The balance distribution also showcases that there are accounts with a negative balance after a transaction, which would only make sense if debt is also included in this value.
According to description of the field balance: “balance after transaction”
1.2.5.1 Transaction Amounts and Counts by Month
# Getting a list of unique years from the dataset
transactions_df["year"] = transactions_df["date"].dt.year
transactions_df["month"] = transactions_df["date"].dt.month
months = [
"Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec",
]
years = sorted(transactions_df["year"].unique())
fig, axs = plt.subplots(
len(years) * 2,
1,
figsize=(8, 6 * len(years)),
sharex=True,
gridspec_kw={"height_ratios": [3, 1] * len(years)},
)
for i, year in enumerate(years):
# Filter transactions for the current year
yearly_transactions = transactions_df[transactions_df["year"] == year]
# Preparing data for the box plot: a list of amounts for each month for the current year
amounts_per_month_yearly = [
yearly_transactions[yearly_transactions["month"] == month]["amount"]
for month in range(1, 13)
]
# Preparing data for the bar chart for the current year
monthly_summary_yearly = (
yearly_transactions.groupby("month")
.agg(TotalAmount=("amount", "sum"), TransactionCount=("amount", "count"))
.reset_index()
)
# Box plot for transaction amounts by month for the current year
axs[i * 2].boxplot(amounts_per_month_yearly, patch_artist=True)
# now with seaborn
# sns.boxplot(data=yearly_transactions, x='month', y='amount', ax=axs[i*2])
axs[i * 2].set_title(f"Transaction Amounts Per Month in {year} (Box Plot)")
axs[i * 2].set_yscale("symlog")
axs[i * 2].set_ylabel("Transaction Amounts (log scale)")
axs[i * 2].grid(True, which="both")
# Bar chart for transaction count by month for the current year
axs[i * 2 + 1].bar(
monthly_summary_yearly["month"],
monthly_summary_yearly["TransactionCount"],
color="tab:red",
alpha=0.6,
)
axs[i * 2 + 1].set_ylabel("Transaction Count")
axs[i * 2 + 1].grid(True, which="both")
# Setting x-ticks and labels for the last bar chart (shared x-axis for all)
axs[-1].set_xticks(range(1, 13))
axs[-1].set_xticklabels(months)
axs[-1].set_xlabel("Month")
plt.tight_layout()
plt.show()fig, axs = plt.subplots(
2,
len(years),
figsize=(8 * len(years) / 2, 7),
sharey="row",
gridspec_kw={"height_ratios": [3, 1]},
)
for i, year in enumerate(years):
# Filter transactions for the current year
yearly_transactions = transactions_df[transactions_df["year"] == year]
# Preparing data for the box plot: a list of amounts for each month for the current year
amounts_per_month_yearly = [
yearly_transactions[yearly_transactions["month"] == month]["amount"]
for month in range(1, 13)
]
# Preparing data for the bar chart for the current year
monthly_summary_yearly = (
yearly_transactions.groupby("month")
.agg(TotalAmount=("amount", "sum"), TransactionCount=("amount", "count"))
.reset_index()
)
# Selecting the appropriate axes for multiple or single year scenarios
ax_box = axs[0, i] if len(years) > 1 else axs[0]
ax_bar = axs[1, i] if len(years) > 1 else axs[1]
ax_box.boxplot(amounts_per_month_yearly, patch_artist=True)
ax_box.set_title(f"{year} (Box Plot)")
ax_box.set_yscale("symlog")
ax_box.set_ylabel("Transaction Amounts (log scale)")
ax_box.grid(True, which="both")
ax_bar.bar(
monthly_summary_yearly["month"],
monthly_summary_yearly["TransactionCount"],
color="tab:red",
alpha=0.6,
)
ax_bar.set_ylabel("Transaction Count")
ax_bar.grid(True, which="both")
# Setting common x-ticks and labels for all axes
ax_bar.set_xticks(range(1, 13))
ax_bar.set_xticklabels(months)
fig.text(0.5, 0.04, "Month", ha="center")
plt.tight_layout()
plt.show()1.2.5.2 Negative Balances
negative_balances = transactions_df[transactions_df["balance"] < 0]
plot_numerical_distributions(negative_balances, ["balance", "amount"])
print(f"Number of transactions with negative balance: {len(negative_balances)}")Number of transactions with negative balance: 2999
There appear to be 2999 transactions which have a negative balance, therefore after the transaction the account balance was negative. This implies that these accounts are in some kind of debt.
1.2.6 Loans
loans_df = read_csv("data/loan.csv")
loans_df["date"] = pd.to_datetime(loans_df["date"], format="%y%m%d")
loans_df["status"] = loans_df["status"].map(
{
"A": "Contract finished, no problems",
"B": "Contract finished, loan not paid",
"C": "Contract running, OK thus-far",
"D": "Contract running, client in debt",
}
)
loans_df.rename(
columns={
"date": "granted_date",
"amount": "amount",
"duration": "duration",
"payments": "monthly_payments",
"status": "status",
},
inplace=True,
)
loans_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 682 entries, 0 to 681
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 loan_id 682 non-null int64
1 account_id 682 non-null int64
2 granted_date 682 non-null datetime64[ns]
3 amount 682 non-null int64
4 duration 682 non-null int64
5 monthly_payments 682 non-null float64
6 status 682 non-null object
dtypes: datetime64[ns](1), float64(1), int64(4), object(1)
memory usage: 37.4+ KB
# todo add some basic eda here
loans_df.head()| loan_id | account_id | granted_date | amount | duration | monthly_payments | status | |
|---|---|---|---|---|---|---|---|
| 0 | 5314 | 1787 | 1993-07-05 | 96396 | 12 | 8033.0 | Contract finished, loan not paid |
| 1 | 5316 | 1801 | 1993-07-11 | 165960 | 36 | 4610.0 | Contract finished, no problems |
| 2 | 6863 | 9188 | 1993-07-28 | 127080 | 60 | 2118.0 | Contract finished, no problems |
| 3 | 5325 | 1843 | 1993-08-03 | 105804 | 36 | 2939.0 | Contract finished, no problems |
| 4 | 7240 | 11013 | 1993-09-06 | 274740 | 60 | 4579.0 | Contract finished, no problems |
loans_df.describe()| loan_id | account_id | granted_date | amount | duration | monthly_payments | |
|---|---|---|---|---|---|---|
| count | 682.000000 | 682.000000 | 682 | 682.000000 | 682.000000 | 682.000000 |
| mean | 6172.466276 | 5824.162757 | 1996-09-29 05:35:43.108504448 | 151410.175953 | 36.492669 | 4190.664223 |
| min | 4959.000000 | 2.000000 | 1993-07-05 00:00:00 | 4980.000000 | 12.000000 | 304.000000 |
| 25% | 5577.500000 | 2967.000000 | 1995-07-04 12:00:00 | 66732.000000 | 24.000000 | 2477.000000 |
| 50% | 6176.500000 | 5738.500000 | 1997-02-06 12:00:00 | 116928.000000 | 36.000000 | 3934.000000 |
| 75% | 6752.500000 | 8686.000000 | 1997-12-12 12:00:00 | 210654.000000 | 48.000000 | 5813.500000 |
| max | 7308.000000 | 11362.000000 | 1998-12-08 00:00:00 | 590820.000000 | 60.000000 | 9910.000000 |
| std | 682.579279 | 3283.512681 | NaN | 113372.406310 | 17.075219 | 2215.830344 |
loans_df.nunique()loan_id 682
account_id 682
granted_date 559
amount 645
duration 5
monthly_payments 577
status 4
dtype: int64
It seems as if one account can have at max one loan.
plot_categorical_variables(loans_df, ["duration", "status"])The distribution of durations seems to be even.
plot_numerical_distributions(loans_df, ["granted_date"])1.2.7 Credit Cards
cards_df = read_csv("data/card.csv")
cards_df["issued"] = pd.to_datetime(
cards_df["issued"], format="%y%m%d %H:%M:%S"
).dt.date
cards_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 892 entries, 0 to 891
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 card_id 892 non-null int64
1 disp_id 892 non-null int64
2 type 892 non-null object
3 issued 892 non-null object
dtypes: int64(2), object(2)
memory usage: 28.0+ KB
cards_df.head()| card_id | disp_id | type | issued | |
|---|---|---|---|---|
| 0 | 1005 | 9285 | classic | 1993-11-07 |
| 1 | 104 | 588 | classic | 1994-01-19 |
| 2 | 747 | 4915 | classic | 1994-02-05 |
| 3 | 70 | 439 | classic | 1994-02-08 |
| 4 | 577 | 3687 | classic | 1994-02-15 |
cards_df.describe()| card_id | disp_id | |
|---|---|---|
| count | 892.000000 | 892.000000 |
| mean | 480.855381 | 3511.862108 |
| std | 306.933982 | 2984.373626 |
| min | 1.000000 | 9.000000 |
| 25% | 229.750000 | 1387.000000 |
| 50% | 456.500000 | 2938.500000 |
| 75% | 684.250000 | 4459.500000 |
| max | 1247.000000 | 13660.000000 |
plot_categorical_variables(cards_df, ["type"])plot_numerical_distributions(cards_df, ["issued"])1.2.8 Demographic data
districts_df = read_csv("data/district.csv")
# Rename columns
# according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
districts_df.rename(
columns={
"A1": "district_id",
"A2": "district_name",
"A3": "region",
"A4": "inhabitants",
"A5": "small_municipalities",
"A6": "medium_municipalities",
"A7": "large_municipalities",
"A8": "huge_municipalities",
"A9": "cities",
"A10": "ratio_urban_inhabitants",
"A11": "average_salary",
"A12": "unemployment_rate_1995",
"A13": "unemployment_rate_1996",
"A14": "entrepreneurs_per_1000_inhabitants",
"A15": "crimes_committed_1995",
"A16": "crimes_committed_1996",
},
inplace=True,
)
for col in [
"unemployment_rate_1995",
"unemployment_rate_1996",
"crimes_committed_1995",
"crimes_committed_1996",
]:
districts_df[col] = pd.to_numeric(districts_df[col], errors="coerce")
districts_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 77 entries, 0 to 76
Data columns (total 16 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 district_id 77 non-null int64
1 district_name 77 non-null object
2 region 77 non-null object
3 inhabitants 77 non-null int64
4 small_municipalities 77 non-null int64
5 medium_municipalities 77 non-null int64
6 large_municipalities 77 non-null int64
7 huge_municipalities 77 non-null int64
8 cities 77 non-null int64
9 ratio_urban_inhabitants 77 non-null float64
10 average_salary 77 non-null int64
11 unemployment_rate_1995 76 non-null float64
12 unemployment_rate_1996 77 non-null float64
13 entrepreneurs_per_1000_inhabitants 77 non-null int64
14 crimes_committed_1995 76 non-null float64
15 crimes_committed_1996 77 non-null int64
dtypes: float64(4), int64(10), object(2)
memory usage: 9.8+ KB
It appears as if there is 1 null value for unemployment rate in 1995 and crimes committed in 1995.
# todo add some basic eda here
districts_df.head()| district_id | district_name | region | inhabitants | small_municipalities | medium_municipalities | large_municipalities | huge_municipalities | cities | ratio_urban_inhabitants | average_salary | unemployment_rate_1995 | unemployment_rate_1996 | entrepreneurs_per_1000_inhabitants | crimes_committed_1995 | crimes_committed_1996 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Hl.m. Praha | Prague | 1204953 | 0 | 0 | 0 | 1 | 1 | 100.0 | 12541 | 0.29 | 0.43 | 167 | 85677.0 | 99107 |
| 1 | 2 | Benesov | central Bohemia | 88884 | 80 | 26 | 6 | 2 | 5 | 46.7 | 8507 | 1.67 | 1.85 | 132 | 2159.0 | 2674 |
| 2 | 3 | Beroun | central Bohemia | 75232 | 55 | 26 | 4 | 1 | 5 | 41.7 | 8980 | 1.95 | 2.21 | 111 | 2824.0 | 2813 |
| 3 | 4 | Kladno | central Bohemia | 149893 | 63 | 29 | 6 | 2 | 6 | 67.4 | 9753 | 4.64 | 5.05 | 109 | 5244.0 | 5892 |
| 4 | 5 | Kolin | central Bohemia | 95616 | 65 | 30 | 4 | 1 | 6 | 51.4 | 9307 | 3.85 | 4.43 | 118 | 2616.0 | 3040 |
districts_df.describe()| district_id | inhabitants | small_municipalities | medium_municipalities | large_municipalities | huge_municipalities | cities | ratio_urban_inhabitants | average_salary | unemployment_rate_1995 | unemployment_rate_1996 | entrepreneurs_per_1000_inhabitants | crimes_committed_1995 | crimes_committed_1996 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 77.000000 | 7.700000e+01 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 76.000000 | 77.000000 | 77.000000 | 76.000000 | 77.000000 |
| mean | 39.000000 | 1.338849e+05 | 48.623377 | 24.324675 | 6.272727 | 1.727273 | 6.259740 | 63.035065 | 9031.675325 | 3.119342 | 3.787013 | 116.129870 | 4850.315789 | 5030.831169 |
| std | 22.371857 | 1.369135e+05 | 32.741829 | 12.780991 | 4.015222 | 1.008338 | 2.435497 | 16.221727 | 790.202347 | 1.665568 | 1.908480 | 16.608773 | 9888.951933 | 11270.796786 |
| min | 1.000000 | 4.282100e+04 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 33.900000 | 8110.000000 | 0.290000 | 0.430000 | 81.000000 | 818.000000 | 888.000000 |
| 25% | 20.000000 | 8.585200e+04 | 22.000000 | 16.000000 | 4.000000 | 1.000000 | 5.000000 | 51.900000 | 8512.000000 | 1.787500 | 2.310000 | 105.000000 | 2029.750000 | 2122.000000 |
| 50% | 39.000000 | 1.088710e+05 | 49.000000 | 25.000000 | 6.000000 | 2.000000 | 6.000000 | 59.800000 | 8814.000000 | 2.825000 | 3.600000 | 113.000000 | 2932.000000 | 3040.000000 |
| 75% | 58.000000 | 1.390120e+05 | 71.000000 | 32.000000 | 8.000000 | 2.000000 | 8.000000 | 73.500000 | 9317.000000 | 3.890000 | 4.790000 | 126.000000 | 4525.500000 | 4595.000000 |
| max | 77.000000 | 1.204953e+06 | 151.000000 | 70.000000 | 20.000000 | 5.000000 | 11.000000 | 100.000000 | 12541.000000 | 7.340000 | 9.400000 | 167.000000 | 85677.000000 | 99107.000000 |
districts_df.nunique()district_id 77
district_name 77
region 8
inhabitants 77
small_municipalities 53
medium_municipalities 36
large_municipalities 17
huge_municipalities 6
cities 11
ratio_urban_inhabitants 70
average_salary 76
unemployment_rate_1995 70
unemployment_rate_1996 73
entrepreneurs_per_1000_inhabitants 44
crimes_committed_1995 75
crimes_committed_1996 76
dtype: int64
plot_numerical_distributions(districts_df, ["crimes_committed_1995"])plot_categorical_variables(districts_df, ["region"])/tmp/ipykernel_1879/945940023.py:33: UserWarning:
set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
We need to differentiate between the domicile of the client and account, as they can be different.
1.3 Data Relationships
Following the documentation of the dataset, there are multiple relationships that need to be validated. https://sorry.vse.cz/~berka/challenge/PAST/index.html
The ERD according to the descriptions on https://sorry.vse.cz/~berka/challenge/PAST/index.html
This ERD shows how the data appears in the dataset:
In order to also validate the relationships from a algorithmic perspective, we can use the following code:
# Verify 1:1 relationships between CLIENT, LOAN and DISPOSITION
assert dispositions_df[
"client_id"
].is_unique, "Each client_id should appear exactly once in the DISPOSITION DataFrame."
assert loans_df[
"account_id"
].is_unique, "Each account_id should appear exactly once in the LOAN DataFrame."
# Verify 1:M relationships between ACCOUNT and DISPOSITION
# assert dispositions['account_id'].is_unique == False, "An account_id should appear more than once in the DISPOSITION DataFrame."
assert (
dispositions_df["account_id"].is_unique == True
), "An account_id should appear once in the DISPOSITION DataFrame."
# TODO check if in accordance to decision to remove disponents from dispositions
# Verify each district_id in ACCOUNT and CLIENT exists in DISTRICT
assert set(accounts_df["district_id"]).issubset(
set(districts_df["district_id"])
), "All district_ids in ACCOUNT should exist in DISTRICT."
assert set(clients_df["district_id"]).issubset(
set(districts_df["district_id"])
), "All district_ids in CLIENT should exist in DISTRICT."
# Verify each account_id in DISPOSITION, ORDER, TRANSACTION, and LOAN exists in ACCOUNT
assert set(dispositions_df["account_id"]).issubset(
set(accounts_df["account_id"])
), "All account_ids in DISPOSITION should exist in ACCOUNT."
assert set(orders_df["account_id"]).issubset(
set(accounts_df["account_id"])
), "All account_ids in ORDER should exist in ACCOUNT."
assert set(transactions_df["account_id"]).issubset(
set(accounts_df["account_id"])
), "All account_ids in TRANSACTION should exist in ACCOUNT."
assert set(loans_df["account_id"]).issubset(
set(accounts_df["account_id"])
), "All account_ids in LOAN should exist in ACCOUNT."
# Verify each client_id in DISPOSITION exists in CLIENT
assert set(dispositions_df["client_id"]).issubset(
set(clients_df["client_id"])
), "All client_ids in DISPOSITION should exist in CLIENT."
# Verify each disp_id in CARD exists in DISPOSITION
assert set(cards_df["disp_id"]).issubset(
set(dispositions_df["disp_id"])
), "All disp_ids in CARD should exist in DISPOSITION."2 Data Preparation: Non-Transactional Data
orders_pivot_df = orders_df.pivot_table(
index="account_id",
columns="k_symbol",
values="debited_amount",
aggfunc="sum",
fill_value=0,
)
orders_pivot_df.columns = [
f"k_symbol_debited_sum_{col.lower()}" for col in orders_pivot_df.columns
]
orders_pivot_df = orders_pivot_df.reset_index() # Use created index as account_id
orders_pivot_df.head()| account_id | k_symbol_debited_sum_household | k_symbol_debited_sum_insurance_payment | k_symbol_debited_sum_leasing | k_symbol_debited_sum_loan_payment | k_symbol_debited_sum_na | |
|---|---|---|---|---|---|---|
| 0 | 1 | 2452.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1 | 2 | 7266.0 | 0.0 | 0.0 | 3372.7 | 0.0 |
| 2 | 3 | 1135.0 | 3539.0 | 0.0 | 0.0 | 327.0 |
| 3 | 4 | 3363.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4 | 5 | 2668.0 | 0.0 | 0.0 | 0.0 | 0.0 |
def merge_non_transactional_data(
clients, districts, dispositions, accounts, orders, loans, cards
):
# Rename district_id for clarity in clients and accounts DataFrames
clients = clients.rename(columns={"district_id": "client_district_id"})
accounts = accounts.rename(columns={"district_id": "account_district_id"})
# Prepare districts dataframe for merge with prefix for clients and accounts
districts_client_prefixed = districts.add_prefix("client_")
districts_account_prefixed = districts.add_prefix("account_")
# Merge district information for clients and accounts with prefixed columns
clients_with_districts = pd.merge(
clients,
districts_client_prefixed,
left_on="client_district_id",
right_on="client_district_id",
how="left",
)
accounts_with_districts = pd.merge(
accounts,
districts_account_prefixed,
left_on="account_district_id",
right_on="account_district_id",
how="left",
)
# Merge cards with dispositions and prefix card-related columns to avoid confusion
cards_prefixed = cards.add_prefix("card_")
dispositions_with_cards = pd.merge(
dispositions,
cards_prefixed,
left_on="disp_id",
right_on="card_disp_id",
how="left",
)
# Merge clients (with district info) with dispositions and cards
# Assuming dispositions might have columns that overlap with clients, prefix those if necessary
clients_dispositions_cards = pd.merge(
dispositions_with_cards, clients_with_districts, on="client_id", how="left"
)
# Merge the above with accounts (with district info) on account_id
accounts_clients_cards = pd.merge(
accounts_with_districts, clients_dispositions_cards, on="account_id", how="left"
)
# Merge orders DataFrame, assuming orders might contain columns that could overlap, prefix as needed
orders_prefixed = orders.add_prefix("order_")
comprehensive_df_with_orders = pd.merge(
accounts_clients_cards,
orders_prefixed,
left_on="account_id",
right_on="order_account_id",
how="left",
)
# Merge loans with the comprehensive dataframe (now including orders) on account_id
# Prefix loan-related columns to maintain clarity
loans_prefixed = loans.add_prefix("loan_")
final_df = pd.merge(
comprehensive_df_with_orders,
loans_prefixed,
left_on="account_id",
right_on="loan_account_id",
how="left",
)
final_df["account_created"] = pd.to_datetime(final_df["account_created"])
final_df["card_issued"] = pd.to_datetime(final_df["card_issued"])
final_df["has_card"] = final_df["card_issued"].notna()
return final_df
non_transactional_df = merge_non_transactional_data(
clients_df,
districts_df,
dispositions_df,
accounts_df,
orders_pivot_df,
loans_df,
cards_df,
)
non_transactional_df.to_csv("data/non_transactional.csv", index=False)
non_transactional_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4500 entries, 0 to 4499
Data columns (total 59 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 account_id 4500 non-null int64
1 account_district_id 4500 non-null int64
2 account_frequency 4500 non-null object
3 account_created 4500 non-null datetime64[ns]
4 account_district_name 4500 non-null object
5 account_region 4500 non-null object
6 account_inhabitants 4500 non-null int64
7 account_small_municipalities 4500 non-null int64
8 account_medium_municipalities 4500 non-null int64
9 account_large_municipalities 4500 non-null int64
10 account_huge_municipalities 4500 non-null int64
11 account_cities 4500 non-null int64
12 account_ratio_urban_inhabitants 4500 non-null float64
13 account_average_salary 4500 non-null int64
14 account_unemployment_rate_1995 4452 non-null float64
15 account_unemployment_rate_1996 4500 non-null float64
16 account_entrepreneurs_per_1000_inhabitants 4500 non-null int64
17 account_crimes_committed_1995 4452 non-null float64
18 account_crimes_committed_1996 4500 non-null int64
19 disp_id 4500 non-null int64
20 client_id 4500 non-null int64
21 type 4500 non-null object
22 card_card_id 892 non-null float64
23 card_disp_id 892 non-null float64
24 card_type 892 non-null object
25 card_issued 892 non-null datetime64[ns]
26 client_district_id 4500 non-null int64
27 sex 4500 non-null object
28 birth_date 4500 non-null datetime64[ns]
29 age 4500 non-null int64
30 client_district_name 4500 non-null object
31 client_region 4500 non-null object
32 client_inhabitants 4500 non-null int64
33 client_small_municipalities 4500 non-null int64
34 client_medium_municipalities 4500 non-null int64
35 client_large_municipalities 4500 non-null int64
36 client_huge_municipalities 4500 non-null int64
37 client_cities 4500 non-null int64
38 client_ratio_urban_inhabitants 4500 non-null float64
39 client_average_salary 4500 non-null int64
40 client_unemployment_rate_1995 4448 non-null float64
41 client_unemployment_rate_1996 4500 non-null float64
42 client_entrepreneurs_per_1000_inhabitants 4500 non-null int64
43 client_crimes_committed_1995 4448 non-null float64
44 client_crimes_committed_1996 4500 non-null int64
45 order_account_id 3758 non-null float64
46 order_k_symbol_debited_sum_household 3758 non-null float64
47 order_k_symbol_debited_sum_insurance_payment 3758 non-null float64
48 order_k_symbol_debited_sum_leasing 3758 non-null float64
49 order_k_symbol_debited_sum_loan_payment 3758 non-null float64
50 order_k_symbol_debited_sum_na 3758 non-null float64
51 loan_loan_id 682 non-null float64
52 loan_account_id 682 non-null float64
53 loan_granted_date 682 non-null datetime64[ns]
54 loan_amount 682 non-null float64
55 loan_duration 682 non-null float64
56 loan_monthly_payments 682 non-null float64
57 loan_status 682 non-null object
58 has_card 4500 non-null bool
dtypes: bool(1), datetime64[ns](4), float64(21), int64(24), object(9)
memory usage: 2.0+ MB
3 Exploratory Data Analysis
3.1 Non-transactional Data
3.1.1 Card Holders
plt.figure()
plt.title("Number of Clients by Card Type")
sns.barplot(
x=["No Card", "Classic/Gold Card Holders", "Junior Card Holders"],
y=[
non_transactional_df["card_type"].isna().sum(),
non_transactional_df["card_type"].isin(["gold", "classic"]).sum(),
non_transactional_df["card_type"].eq("junior").sum(),
],
)
# ensure that the number of clients is shown on the bars
for i, v in enumerate(
[
non_transactional_df["card_type"].isna().sum(),
non_transactional_df["card_type"].isin(["gold", "classic"]).sum(),
non_transactional_df["card_type"].eq("junior").sum(),
]
):
plt.text(i, v + 10, str(v), ha="center", va="bottom")
plt.show()Looking at the distribution of card holders in general we can see that the most clients are not in a possession of a credit card.
plt.figure()
plt.title(
f'Distribution of Age for Junior Card Holders\n total count = {len(non_transactional_df[non_transactional_df["card_type"] == "junior"])}'
)
sns.histplot(
non_transactional_df[non_transactional_df["card_type"] == "junior"]["age"],
kde=True,
bins=30,
)
plt.xlabel("Age of Client (presumably in 1999)")
plt.show()Looking at the age distribution of Junior Card holders paints a picture on this group, however only looking at the current age may be misleading as we need to understand how old they were when the card was issued to determine if they could have been eligble for a Classic/Gold card (at least 18 when the card was issued).
non_transactional_df["card_issued"] = pd.to_datetime(
non_transactional_df["card_issued"]
)
non_transactional_df["age_at_card_issuance"] = (
non_transactional_df["card_issued"] - non_transactional_df["birth_date"]
)
non_transactional_df["age_at_card_issuance"] = (
non_transactional_df["age_at_card_issuance"].dt.days // 365
)
plt.figure()
plt.title(
f'Distribution of Age at Card Issuance for Junior Card Holders\n total count = {len(non_transactional_df[non_transactional_df["card_type"] == "junior"])}'
)
sns.histplot(
non_transactional_df[non_transactional_df["card_type"] == "junior"][
"age_at_card_issuance"
],
kde=True,
bins=30,
)
plt.xlabel("Age at Card Issuance")
plt.show()Here we can see that roughly 1/3 of the Junior Card holders were not of legal age (assuming legal age is 18) when receiving their Junior Card.
plt.figure()
plt.title(
f"Distribution of Age at Card Issuance for All Card Types\n total count = {len(non_transactional_df)}"
)
sns.histplot(
non_transactional_df[non_transactional_df["card_type"] == "junior"][
"age_at_card_issuance"
],
kde=True,
bins=10,
color="blue",
label="Junior Card Holders",
)
sns.histplot(
non_transactional_df[non_transactional_df["card_type"] != "junior"][
"age_at_card_issuance"
],
kde=True,
bins=30,
color="red",
label="Non-Junior Card Holders",
)
plt.legend()
plt.xlabel("Age at Card Issuance")
plt.show()Comparing the age at issue date between Junior and non-Junior (Classic/Gold) card holders shows that there is no overlap between the two groups, which makes intutively sense.
Therefore removing the subset of Junior Cards seems as valid as there is no reason to believe that there are Junior Cards issued wrongly, the subset being relatively small compared to the remaining issued cards and the fact that our target is specifically Classic/Gold Card owners.
before_len = len(non_transactional_df)
non_transactional_df = non_transactional_df[
non_transactional_df["card_type"] != "junior"
]
data_reduction["Junior Card Holders"] = -(before_len - len(non_transactional_df))
del before_lenLooking at the age distribution of Junior card holders and their occurence in comparison it seems valid to remove them as they are not the target group and make up a small subset of the complete dataset.
3.1.2 Time factors on Card Status
The time between creating an account and issuing a card may also be important when filtering customers based on their history. We should avoid filtering out potentially interesting periods and understand how the timespans between account creation and card issuance are distributed.
non_transactional_w_cards_df = non_transactional_df[
non_transactional_df["card_issued"].notna()
& non_transactional_df["account_created"].notna()
]
non_transactional_w_cards_df["duration_days"] = (
non_transactional_w_cards_df["card_issued"]
- non_transactional_w_cards_df["account_created"]
).dt.days
plt.figure(figsize=(8, 6))
sns.histplot(
non_transactional_w_cards_df["duration_days"], bins=50, edgecolor="black", kde=True
)
plt.title("Distribution of Duration Between Account Creation and Card Issuance")
plt.xlabel("Duration in Days")
plt.ylabel("Frequency")
plt.tight_layout()
plt.show()/tmp/ipykernel_1879/17211290.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
The histogram displays a distribution with multiple peaks, indicating that there are several typical time frames for card issuance after account creation. The highest peak occurs within the first 250 days, suggesting that a significant number of cards are issued during this period. The frequency decreases as duration increases, with noticeable peaks that may correspond to specific processing batch cycles or policy changes over time. The distribution also has a long tail, suggesting that in some cases, card issuance can take a very long time.
Analyzing the length of time a client has been with the bank in relation to their account creation date and card ownership can provide valuable insights for a bank’s customer relationship management and product targeting strategies. Long-standing clients may exhibit different banking behaviors, such as product adoption and loyalty patterns, compared to newer clients.
max_account_creation_date = non_transactional_df["card_issued"].max()
non_transactional_df["client_tenure_years_relative"] = (
max_account_creation_date - non_transactional_df["account_created"]
).dt.days / 365.25
plt.figure()
ax = sns.histplot(
data=non_transactional_df,
x="client_tenure_years_relative",
hue="has_card",
multiple="stack",
binwidth=1,
stat="percent",
)
# Call the function to add labels
add_percentage_labels(ax, non_transactional_df["has_card"].unique())
# Additional plot formatting
plt.title("Client Tenure Relative to Latest Card Issued Date and Card Ownership")
plt.xlabel("Client Tenure (Years, Relative to Latest Card Issuance)")
plt.ylabel("Percentage of Clients")
# Display the plot
plt.show()The bar chart shows the tenure of clients in years, categorized by whether they own a credit card (True) or not (False). Each bar represents the percentage of clients within a specific tenure range, allowing for comparison of the distribution of card ownership among clients with different lengths of association with the bank.
3.1.3 Demographics
Using the available demographic data, we can investigate the potential correlation between demographic data and card status. The average salary may indicate a difference between cardholders and non-cardholders, as it is reasonable to assume that cardholders have a higher average salary than non-cardholders.
plt.figure()
sns.boxplot(x="has_card", y="client_average_salary", data=non_transactional_df)
plt.title("Average Salary in Client's Region by Card Ownership")
plt.xlabel("Has Card")
plt.ylabel("Average Salary")
plt.xticks([0, 1], ["No Card Owner", "Card Owner"])
plt.show()The box plot compares the average salaries of clients who own a credit card with those who do not. Both groups have a substantial overlap in salary ranges, suggesting that while there might be a trend for card owners to have higher salaries, the difference is not significant. The median salary for card owners is slightly higher than that for non-card owners, as indicated by the median line within the respective boxes.
Both distributions have outliers on the higher end, indicating that some individuals have salaries significantly above the average in both groups. However, these outliers do not dominate the general trend.
It should also be noted that this plot assumes that the average salary of the region’s clients remained constant over the years, which is unlikely to be true.
The group of bar charts represents the distribution of credit card ownership across various demographics, showing the percentage of clients with and without cards within different age groups, sexes, and regions.
non_transactional_df["age_group"] = pd.cut(
non_transactional_df["age"],
bins=[0, 25, 40, 55, 70, 100],
labels=["<25", "25-40", "40-55", "55-70", ">70"],
)
plt.figure(figsize=(8, 12))
# Age Group
plt.subplot(3, 1, 1)
age_group_counts = (
non_transactional_df.groupby(["age_group", "has_card"]).size().unstack(fill_value=0)
)
age_group_percentages = (age_group_counts.T / age_group_counts.sum(axis=1)).T * 100
age_group_plot = age_group_percentages.plot(kind="bar", stacked=True, ax=plt.gca())
age_group_plot.set_title("Card Ownership by Age Group")
age_group_plot.set_ylabel("Percentage")
add_percentage_labels(age_group_plot, non_transactional_df["has_card"].unique())
# Sex
plt.subplot(3, 1, 2)
sex_counts = (
non_transactional_df.groupby(["sex", "has_card"]).size().unstack(fill_value=0)
)
sex_percentages = (sex_counts.T / sex_counts.sum(axis=1)).T * 100
sex_plot = sex_percentages.plot(kind="bar", stacked=True, ax=plt.gca())
sex_plot.set_title("Card Ownership by Sex")
sex_plot.set_ylabel("Percentage")
add_percentage_labels(sex_plot, non_transactional_df["has_card"].unique())
# Client Region
plt.subplot(3, 1, 3)
region_counts = (
non_transactional_df.groupby(["client_region", "has_card"])
.size()
.unstack(fill_value=0)
)
region_percentages = (region_counts.T / region_counts.sum(axis=1)).T * 100
region_plot = region_percentages.plot(kind="bar", stacked=True, ax=plt.gca())
region_plot.set_title("Card Ownership by Client Region")
region_plot.set_ylabel("Percentage")
region_plot.tick_params(axis="x", rotation=45)
add_percentage_labels(region_plot, non_transactional_df["has_card"].unique())
plt.tight_layout()
plt.show()/tmp/ipykernel_1879/271218705.py:12: FutureWarning:
The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
Card Ownership by Age Group: The bar chart displays the proportion of cardholders in different age groups. The percentage of cardholders is lowest in the age group of over 70, followed by the age group of 55-70, indicating that card ownership is more prevalent among younger demographics.
Card Ownership by Sex: The bar chart shows the breakdown of card ownership by sex. The data reveals that the percentage of cardholders is comparable between both sexes, and no significant difference is present.
Card Ownership by Region The bar chart at the bottom illustrates card ownership across different regions, showing a relatively consistent pattern among most regions.
3.1.4 Impact of Loans / Debt
simplified_loan_status_mapping = {
"Contract finished, no problems": "Finished",
"Contract finished, loan not paid": "Not Paid",
"Contract running, OK thus-far": "Running",
"Contract running, client in debt": "In Debt",
"No Loan": "No Loan",
}
non_transactional_df["loan_status_simplified"] = non_transactional_df[
"loan_status"
].map(simplified_loan_status_mapping)
# this variable wants to kill itself
loan_status_simplified_card_ownership_counts = (
non_transactional_df.groupby(["loan_status_simplified", "has_card"])
.size()
.unstack(fill_value=0)
)
loan_status_simplified_card_ownership_percentages = (
loan_status_simplified_card_ownership_counts.T
/ loan_status_simplified_card_ownership_counts.sum(axis=1)
).T * 100
loan_status_simplified_card_ownership_percentages.plot(
kind="bar", stacked=True, figsize=(8, 6)
)
plt.title("Interaction Between Simplified Loan Status and Card Ownership")
plt.xlabel("Simplified Loan Status")
plt.ylabel("Percentage of Clients")
plt.xticks(rotation=45)
plt.legend(title="Has Card", labels=["No Card", "Has Card"])
plt.tight_layout()
plt.show()3.2 Transactional Data
TODO: Add more EDA for transactional data
zero_amount_transactions_df = transactions_df[transactions_df["amount"] == 0]
zero_amount_transactions_info = {
"total_zero_amount_transactions": len(zero_amount_transactions_df),
"unique_accounts_with_zero_amount": zero_amount_transactions_df[
"account_id"
].nunique(),
"transaction_type_distribution": zero_amount_transactions_df[
"transaction_type"
].value_counts(normalize=True),
"operation_distribution": zero_amount_transactions_df["operation"].value_counts(
normalize=True
),
"k_symbol_distribution": zero_amount_transactions_df["k_symbol"].value_counts(
normalize=True
),
}
zero_amount_transactions_info, len(zero_amount_transactions_info)({'total_zero_amount_transactions': 14,
'unique_accounts_with_zero_amount': 12,
'transaction_type_distribution': transaction_type
Withdrawal 0.714286
Credit 0.285714
Name: proportion, dtype: float64,
'operation_distribution': operation
Withdrawal in Cash 0.714286
NA 0.285714
Name: proportion, dtype: float64,
'k_symbol_distribution': k_symbol
Sanction Interest 0.714286
Interest Credited 0.285714
Name: proportion, dtype: float64},
5)
accounts_with_zero_amount_transactions = accounts_df[
accounts_df["account_id"].isin(zero_amount_transactions_df["account_id"].unique())
]
accounts_with_zero_amount_transactions| account_id | district_id | account_frequency | account_created | |
|---|---|---|---|---|
| 178 | 5369 | 54 | MONTHLY_ISSUANCE | 1993-02-25 |
| 289 | 5483 | 13 | MONTHLY_ISSUANCE | 1993-03-28 |
| 496 | 5129 | 68 | MONTHLY_ISSUANCE | 1993-06-08 |
| 513 | 1475 | 1 | WEEKLY_ISSUANCE | 1993-06-14 |
| 799 | 9337 | 30 | MONTHLY_ISSUANCE | 1993-09-13 |
| 896 | 102 | 11 | MONTHLY_ISSUANCE | 1993-10-16 |
| 986 | 8957 | 1 | MONTHLY_ISSUANCE | 1993-11-13 |
| 2033 | 5125 | 1 | MONTHLY_ISSUANCE | 1995-09-14 |
| 2300 | 9051 | 5 | WEEKLY_ISSUANCE | 1996-01-17 |
| 2651 | 3859 | 53 | MONTHLY_ISSUANCE | 1996-04-23 |
| 3212 | 6083 | 6 | WEEKLY_ISSUANCE | 1996-09-19 |
| 3342 | 1330 | 68 | MONTHLY_ISSUANCE | 1996-10-22 |
# Clean up unnecessary variables
del accounts_with_zero_amount_transactions
del zero_amount_transactions_df
del zero_amount_transactions_infoValidating first transactions where the amount equals the balance is essential for the integrity of our aggregated data analysis. This specific assertion underpins the reliability of our subsequent aggregation operations by ensuring each account’s financial history starts from a verifiable point.
def validate_first_transactions(transactions):
"""
Validates that for each account in the transactions DataFrame, there is at least
one transaction where the amount equals the balance on the account's first transaction date.
Parameters:
- transactions (pd.DataFrame): DataFrame containing transaction data with columns
'account_id', 'date', 'amount', and 'balance'.
Raises:
- AssertionError: If not every account has a first transaction where the amount equals the balance.
"""
first_dates = (
transactions.groupby("account_id")["date"].min().reset_index(name="first_date")
)
first_trans = pd.merge(transactions, first_dates, how="left", on=["account_id"])
first_trans_filtered = first_trans[
(first_trans["date"] == first_trans["first_date"])
& (first_trans["amount"] == first_trans["balance"])
]
first_trans_filtered = first_trans_filtered.drop_duplicates(subset=["account_id"])
unique_accounts = transactions["account_id"].nunique()
assert (
unique_accounts == first_trans_filtered["account_id"].nunique()
), "Not every account has a first transaction where the amount equals the balance."
return "Validation successful: Each account has a first transaction where the amount equals the balance."
validate_first_transactions(transactions_df)'Validation successful: Each account has a first transaction where the amount equals the balance.'
We can confirm the truth of the assertions made. It is certain that there is a transaction with an amount equal to the balance in the transaction history of any account on the first date.
## DEPENDENCY 1 TODO REMOVE FOR MERGE
import json
# save transactions_df to temp as parquet
transactions_df.to_parquet("temp/transactions.parquet")
accounts_df.to_parquet("temp/accounts.parquet")
non_transactional_df.to_parquet("temp/non_transactional.parquet")
# save data reduction
with open("temp/data_reduction.json", "w") as f:
json.dump(data_reduction, f)## DEPENDENCY #TODO REMOVE FOR MERGE
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import json
transactions_df = pd.read_parquet("temp/transactions.parquet")
accounts_df = pd.read_parquet("temp/accounts.parquet")
non_transactional_df = pd.read_parquet("temp/non_transactional.parquet")
# read data_reduction from temp/data_reduction.json
with open("temp/data_reduction.json", "r") as f:
data_reduction = json.load(f)4 Data Preparation: Transactional Data
4.1 Set artificial issue date for non-card holders
def add_months_since_account_to_card(df):
df["months_since_account_to_card"] = df.apply(
lambda row: (
(
row["card_issued"].to_period("M")
- row["account_created"].to_period("M")
).n
if pd.notnull(row["card_issued"]) and pd.notnull(row["account_created"])
else np.nan
),
axis=1,
)
return df
def filter_clients_without_sufficient_history(
non_transactional_df, min_history_months=25
):
if "months_since_account_to_card" not in non_transactional_df.columns:
print(
"Warning: months_since_account_to_card column not found. Calculating history length."
)
non_transactional_df = add_months_since_account_to_card(non_transactional_df)
count_before = len(non_transactional_df)
filtered_df = non_transactional_df[
non_transactional_df["months_since_account_to_card"].isnull()
| (non_transactional_df["months_since_account_to_card"] >= min_history_months)
]
print(
f"Filtered out {count_before - len(filtered_df)} records with less than {min_history_months} months of history. Percentage: {(count_before - len(filtered_df)) / count_before * 100:.2f}%."
)
return filtered_df
before_len = len(non_transactional_df)
non_transactional_w_sufficient_history_df = filter_clients_without_sufficient_history(
non_transactional_df
)
data_reduction["Clients without sufficient history"] = -(
before_len - len(non_transactional_w_sufficient_history_df)
)
del before_lenWarning: months_since_account_to_card column not found. Calculating history length.
Filtered out 419 records with less than 25 months of history. Percentage: 9.62%.
non_transactional_w_card_df = non_transactional_w_sufficient_history_df.dropna(
subset=["card_issued"]
).copy()
plt.figure(figsize=(8, 6))
sns.histplot(
non_transactional_w_card_df["months_since_account_to_card"], kde=True, bins=30
)
plt.title(
"Distribution of Months from Account Creation to Card Issuance (for Card Holders)"
)
plt.xlabel("Months")
plt.ylabel("Count")
plt.grid(True)
plt.tight_layout()
plt.show()4.2 Match by similar transaction activity
The following approaches were considered to match non-card holders with card holders:
- Looking at the distributions above extract the amount of history a buyer most likely has at the issue data of the card
- For each non buyer, find a buyer which was active in a similar time window (Jaccard similarity on the Year-Month sets). Instead of looking at the full activity of a buyer, we only look at the pre-purchase activity as there is reason to believe that clients may change their patterns after purchasing date and therefore add unwanted bias.
The second approach is chosen as it is provides an intuitive way to match clients based on their activity which is not only explainable but also provides a way to match clients based on their behavior. It strikes a balance of not finding a perfect match but a good enough match to focus on the discriminative features of the data.
The following image serves as an technical overview of the matching process: 
The process emphasizes matching based on the timing of activity, rather than a wide array of characteristics. By identifying when both existing cardholders and non-cardholders interacted with the bank, we can infer a level of behavioral alignment that extends beyond mere transactional data. This alignment suggests a shared response to external conditions.
The resolution of the activity matrix is a binary matrix where each row represents a client and each column represents a month. A value of 1 indicates activity in a given month, while 0 indicates inactivity. Therefore we concentrate on the periods during which clients engage with the bank in the form of transactions
Assumption: This assumes that clients active during similar periods might be influenced by the same economic and societal conditions, providing a more nuanced foundation for establishing connections between current cardholders and potential new ones.
4.2.1 Construction of the Activity Matrix
The activity matrix serves as the foundation of our matching process, mapping out the engagement of clients with our services over time. It is constructed from transaction data, organizing client interactions into a structured format that highlights periods of activity.
Data Aggregation: We start with transaction data, which records each client’s interactions across various months. This data includes every transaction made by both current cardholders and potential non-cardholders.
Temporal Transformation: Each transaction is associated with a specific date. These dates are then transformed into monthly periods, consolidating daily transactions into a monthly view of activity. This step simplifies the data, focusing on the presence of activity within each month rather than the specific dates or frequencies of transactions.
Matrix Structure: The transformed data is arranged into a matrix format. Rows represent individual clients, identified by their account IDs. Columns correspond to monthly periods, spanning the entire range of months covered by the transaction data.
Activity Indication: In the matrix, a cell value is set to indicate the presence of activity for a given client in a given month. If a client made one or more transactions in a month, the corresponding cell is marked to reflect this activity. The absence of transactions for a client in a month leaves the cell unmarked.
Binary Representation: The final step involves converting the activity indicators into a binary format. Active months are represented by a ‘1’, indicating the presence of transactions, while inactive months are denoted by a ‘0’, indicating no transactions.
The heatmap provided offers a visual representation of the activity matrix for clients, depicting the levels of engagement over various periods.
Diagonal Trend: There is a distinct diagonal pattern, indicating that newer accounts (those created more recently) have fewer periods of activity. This makes sense as these accounts have not had the opportunity to transact over the earlier periods displayed on the heatmap.
Darker Areas (Purple): These represent periods of inactivity where clients did not engage. The darker the shade, the less activity occurred in that particular period for the corresponding set of accounts.
Brighter Areas (Yellow): In contrast, the brighter areas denote periods of activity. A brighter shade implies more clients were active during that period.
Account Creation Date: Clients are sorted by their account creation date. Those who joined earlier are at the top, while more recent clients appear toward the bottom of the heatmap.
def prepare_activity_matrix(transactions):
"""
Create an activity matrix from transaction data.
The function transforms transaction data into a binary matrix that indicates
whether an account was active in a given month.
Parameters:
- transactions (pd.DataFrame): A DataFrame containing the transaction data.
Returns:
- pd.DataFrame: An activity matrix with accounts as rows and months as columns.
"""
transactions["month_year"] = transactions["date"].dt.to_period("M")
transactions["active"] = 1
activity_matrix = transactions.pivot_table(
index="account_id", columns="month_year", values="active", fill_value=0
)
activity_matrix.columns = [f"active_{str(col)}" for col in activity_matrix.columns]
return activity_matrix
def plot_activity_matrix(activity_matrix):
sparse_matrix = activity_matrix.astype(bool)
plt.figure(figsize=(8, 8))
sns.heatmap(sparse_matrix, cmap="viridis", cbar=True, yticklabels=False)
plt.title(f"Activity Matrix across all clients sorted by account creation date")
plt.xlabel("Period")
plt.ylabel("Accounts")
plt.tight_layout()
plt.show()
activity_matrix = prepare_activity_matrix(transactions_df)
plot_activity_matrix(activity_matrix)4.2.2 Eligibility Criteria
After constructing the activity matrix, we check for eligibility of non-cardholders to be matched with cardholders. This ensures alignment for later model construction. The eligibility criteria are as follows:
- Account History: Non-cardholders must have an established history of interaction, with at least 25 months of history between account creation and card issuance (12 months (= New customer period) + 13 months (= one year of history) + 1 month (Lag period)).
- Account Creation Date: The account creation date of a non-cardholder must precede the card issuance date of the cardholder as this is a prerequisite for the matching process to work correctly when we set the issue date for non-card holders.
from sklearn.metrics import pairwise_distances
from tqdm import tqdm
ELIGIBILITY_THRESHOLD_HIST_MONTHS = 25
def check_eligibility_for_matching(non_cardholder, cardholder, verbose=False):
"""
Determine if a non-cardholder is eligible for matching with a cardholder.
This function checks whether the card issuance to a cardholder occurred at least
25 months after the non-cardholder's account was created.
Parameters:
- non_cardholder (pd.Series): A data series containing the non-cardholder's details.
- cardholder (pd.Series): A data series containing the cardholder's details.
- verbose (bool): If True, print detailed eligibility information. Default is False.
Returns:
- bool: True if the non-cardholder is eligible for matching, False otherwise.
"""
if cardholder["card_issued"] <= non_cardholder["account_created"]:
return False
period_diff = (
cardholder["card_issued"].to_period("M")
- non_cardholder["account_created"].to_period("M")
).n
if verbose:
print(
f"Card issued: {cardholder['card_issued']}, Account created: {non_cardholder['account_created']}, Period diff: {period_diff}, Eligible: {period_diff >= ELIGIBILITY_THRESHOLD_HIST_MONTHS}"
)
return period_diff >= ELIGIBILITY_THRESHOLD_HIST_MONTHS4.2.3 Matching Process
Next up we will implement the matching process. Our matching utilizes the Jaccard similarity index to compare activity patterns: We compare a vector representing an existing cardholder’s monthly activity against a matrix of non-cardholders’ activity patterns. Here we only consider the activity from the first transaction period across all customers to the card issue date.
The Jaccard similarity index is calculated as the intersection of active months divided by the union of active months between the two clients. This index ranges from 0 to 1, with higher values indicating greater similarity in activity patterns.
\[J(A, B) = \frac{|A \cap B|}{|A \cup B|}\]
The function match_cardholders_with_non_cardholders will perform the following steps:
- Data Preparation: The function prepares the activity matrix and splits the non-cardholders into two groups: those with and without cards.
- Matching Process: For each cardholder, the function calculates the Jaccard similarity between their activity pattern and those of eligible non-cardholders. It then selects the top N similar non-cardholders and randomly assigns one match per cardholder.
- Match Selection: The function selects a non-cardholder match for each cardholder based on the Jaccard similarity scores. It ensures that each non-cardholder is matched only once and that the top N similar non-cardholders are considered for matching.
- The selection among the top N similar non-cardholders is done randomly to avoid bias. This process is defined in the
select_non_cardholdersfunction. - The function also checks for the eligibility as defined above.
- If no eligible non-cardholders are found, the function prints a warning message.
- The selection among the top N similar non-cardholders is done randomly to avoid bias. This process is defined in the
- Output: The function returns a list of tuples containing the matched cardholder and non-cardholder client IDs along with their similarity scores.
def select_non_cardholders(
distances,
eligible_non_cardholders,
matches,
matched_applicants,
cardholder,
without_card_activity,
top_n,
):
"""
Randomly select a non-cardholder match for a cardholder from the top N eligible candidates.
Parameters:
- distances (np.array): An array of Jaccard distances between a cardholder and non-cardholders.
- eligible_non_cardholders (list): A list of indices for non-cardholders who are eligible for matching.
- matches (list): A list to which the match will be appended.
- matched_applicants (set): A set of indices for non-cardholders who have already been matched.
- cardholder (pd.Series): The data series of the current cardholder.
- without_card_activity (pd.DataFrame): A DataFrame of non-cardholders without card issuance.
- top_n (int): The number of top similar non-cardholders to consider for matching.
Returns:
- None: The matches list is updated in place with the selected match.
"""
eligible_distances = distances[eligible_non_cardholders]
sorted_indices = np.argsort(eligible_distances)[:top_n]
if sorted_indices.size > 0:
selected_index = np.random.choice(sorted_indices)
actual_selected_index = eligible_non_cardholders[selected_index]
if actual_selected_index not in matched_applicants:
matched_applicants.add(actual_selected_index)
applicant = without_card_activity.iloc[actual_selected_index]
similarity = 1 - eligible_distances[selected_index]
matches.append(
(cardholder["client_id"], applicant["client_id"], similarity)
)
def match_cardholders_with_non_cardholders(non_transactional, transactions, top_n=5):
"""
Match cardholders with non-cardholders based on the similarity of their activity patterns.
The function creates an activity matrix, identifies eligible non-cardholders, calculates
the Jaccard similarity to find matches, and randomly selects one match per cardholder
from the top N similar non-cardholders.
Parameters:
- non_transactional (pd.DataFrame): A DataFrame containing non-cardholders.
- transactions (pd.DataFrame): A DataFrame containing transactional data.
- top_n (int): The number of top similar non-cardholders to consider for matching.
Returns:
- list: A list of tuples with the cardholder and matched non-cardholder client IDs and similarity scores.
"""
with_card = non_transactional[non_transactional["card_issued"].notna()]
without_card = non_transactional[non_transactional["card_issued"].isna()]
activity_matrix = prepare_activity_matrix(transactions)
with_card_activity = with_card.join(activity_matrix, on="account_id", how="left")
without_card_activity = without_card.join(
activity_matrix, on="account_id", how="left"
)
matched_non_cardholders = set()
matches = []
for idx, cardholder in tqdm(
with_card_activity.iterrows(),
total=len(with_card_activity),
desc="Matching cardholders",
):
issue_period = cardholder["card_issued"].to_period("M")
eligible_cols = [
col
for col in activity_matrix
if col.startswith("active") and pd.Period(col.split("_")[1]) <= issue_period
]
if not eligible_cols:
print(
f"No eligible months found for cardholder client_id {cardholder['client_id']}."
)
continue
cardholder_vector = cardholder[eligible_cols].values.reshape(1, -1)
non_cardholder_matrix = without_card_activity[eligible_cols].values
cardholder_vector = np.where(cardholder_vector > 0, 1, 0).astype(bool)
non_cardholder_matrix = np.where(non_cardholder_matrix > 0, 1, 0).astype(bool)
assert (
cardholder_vector.shape[1] == non_cardholder_matrix.shape[1]
), "Dimension mismatch between cardholder and applicant activity matrix."
distances = pairwise_distances(
cardholder_vector, non_cardholder_matrix,
metric="jaccard", n_jobs=-1
).flatten()
eligible_non_cardholders = [
i
for i, applicant in without_card_activity.iterrows()
if check_eligibility_for_matching(applicant, cardholder)
and i not in matched_non_cardholders
]
if eligible_non_cardholders:
select_non_cardholders(
distances,
eligible_non_cardholders,
matches,
matched_non_cardholders,
cardholder,
without_card_activity,
top_n,
)
else:
print(
f"No eligible non-cardholders found for cardholder client_id {cardholder['client_id']}."
)
return matchesTODO: Visualise the matching process
The matching process is executed, and the results are stored in the matched_non_card_holders_df DataFrame. The percentage of clients with a card issued before and after matching is calculated to assess the impact of the matching process. We expect the percentage of clients with a card issued to increase by 100% after matching, as each non-cardholder should be matched with a cardholder.
Last but not least we set the artificial card issue date for each non-cardholder based on the matching results.
def set_artificial_issue_dates(non_transactional_df, matches):
"""
Augment the non-transactional DataFrame with artificial card issue dates based on matching results.
Each matched non-cardholder is assigned a card issue date corresponding to their matched
cardholder. The 'has_card' flag for each non-cardholder is updated accordingly.
Parameters:
- non_transactional_df (pd.DataFrame): The DataFrame of non-cardholders to augment.
- matches (list): A list of tuples containing the matched cardholder and non-cardholder IDs and similarity scores.
Returns:
- pd.DataFrame: The augmented DataFrame with artificial card issue dates.
"""
augmented_df = non_transactional_df.copy()
augmented_df["has_card"] = True
for cardholder_id, non_cardholder_id, _ in matches:
card_issue_date = augmented_df.loc[
augmented_df["client_id"] == cardholder_id, "card_issued"
].values[0]
augmented_df.loc[
augmented_df["client_id"] == non_cardholder_id, ["card_issued", "has_card"]
] = [card_issue_date, False]
return augmented_df
matched_non_card_holders_df = match_cardholders_with_non_cardholders(
non_transactional_w_sufficient_history_df, transactions_df
)
print(
f"Percentage of clients with card issued: {non_transactional_w_sufficient_history_df['card_issued'].notna().mean() * 100:.2f}%"
)
matched_non_card_holders_w_issue_date_df = set_artificial_issue_dates(
non_transactional_w_sufficient_history_df, matched_non_card_holders_df
)
print(
f"Percentage of clients with card issued after matching: {matched_non_card_holders_w_issue_date_df['card_issued'].notna().mean() * 100:.2f}%"
)Matching cardholders: 0%| | 0/328 [00:00<?, ?it/s]Matching cardholders: 0%| | 1/328 [00:00<01:53, 2.87it/s]Matching cardholders: 1%| | 2/328 [00:00<01:53, 2.87it/s]Matching cardholders: 1%| | 3/328 [00:01<01:51, 2.91it/s]Matching cardholders: 1%| | 4/328 [00:01<01:44, 3.11it/s]Matching cardholders: 2%|▏ | 5/328 [00:01<01:40, 3.23it/s]Matching cardholders: 2%|▏ | 6/328 [00:01<01:43, 3.11it/s]Matching cardholders: 2%|▏ | 7/328 [00:02<01:44, 3.07it/s]Matching cardholders: 2%|▏ | 8/328 [00:02<01:45, 3.03it/s]Matching cardholders: 3%|▎ | 9/328 [00:02<01:34, 3.38it/s]Matching cardholders: 3%|▎ | 10/328 [00:03<01:26, 3.66it/s]Matching cardholders: 3%|▎ | 11/328 [00:03<01:33, 3.41it/s]Matching cardholders: 4%|▎ | 12/328 [00:03<01:37, 3.25it/s]Matching cardholders: 4%|▍ | 13/328 [00:04<01:58, 2.67it/s]Matching cardholders: 4%|▍ | 14/328 [00:04<01:50, 2.85it/s]Matching cardholders: 5%|▍ | 15/328 [00:04<01:48, 2.89it/s]Matching cardholders: 5%|▍ | 16/328 [00:05<01:36, 3.25it/s]Matching cardholders: 5%|▌ | 17/328 [00:05<01:27, 3.56it/s]Matching cardholders: 5%|▌ | 18/328 [00:05<01:23, 3.70it/s]Matching cardholders: 6%|▌ | 19/328 [00:05<01:28, 3.47it/s]Matching cardholders: 6%|▌ | 20/328 [00:06<01:31, 3.36it/s]Matching cardholders: 6%|▋ | 21/328 [00:06<01:35, 3.22it/s]Matching cardholders: 7%|▋ | 22/328 [00:06<01:37, 3.14it/s]Matching cardholders: 7%|▋ | 23/328 [00:07<01:39, 3.08it/s]Matching cardholders: 7%|▋ | 24/328 [00:07<01:40, 3.02it/s]Matching cardholders: 8%|▊ | 25/328 [00:07<01:40, 3.01it/s]Matching cardholders: 8%|▊ | 26/328 [00:08<01:30, 3.32it/s]Matching cardholders: 8%|▊ | 27/328 [00:08<01:25, 3.51it/s]Matching cardholders: 9%|▊ | 28/328 [00:08<01:29, 3.35it/s]Matching cardholders: 9%|▉ | 29/328 [00:09<01:29, 3.34it/s]Matching cardholders: 9%|▉ | 30/328 [00:09<01:32, 3.22it/s]Matching cardholders: 9%|▉ | 31/328 [00:09<01:31, 3.25it/s]Matching cardholders: 10%|▉ | 32/328 [00:09<01:24, 3.52it/s]Matching cardholders: 10%|█ | 33/328 [00:10<01:26, 3.42it/s]Matching cardholders: 10%|█ | 34/328 [00:10<01:27, 3.36it/s]Matching cardholders: 11%|█ | 35/328 [00:10<01:38, 2.97it/s]Matching cardholders: 11%|█ | 36/328 [00:11<01:29, 3.27it/s]Matching cardholders: 11%|█▏ | 37/328 [00:11<01:31, 3.17it/s]Matching cardholders: 12%|█▏ | 38/328 [00:11<01:33, 3.11it/s]Matching cardholders: 12%|█▏ | 39/328 [00:12<01:29, 3.25it/s]Matching cardholders: 12%|█▏ | 40/328 [00:12<01:29, 3.22it/s]Matching cardholders: 12%|█▎ | 41/328 [00:12<01:31, 3.15it/s]Matching cardholders: 13%|█▎ | 42/328 [00:13<01:31, 3.12it/s]Matching cardholders: 13%|█▎ | 43/328 [00:13<01:32, 3.07it/s]Matching cardholders: 13%|█▎ | 44/328 [00:13<01:26, 3.27it/s]Matching cardholders: 14%|█▎ | 45/328 [00:13<01:22, 3.42it/s]Matching cardholders: 14%|█▍ | 46/328 [00:14<01:19, 3.57it/s]Matching cardholders: 14%|█▍ | 47/328 [00:14<01:23, 3.35it/s]Matching cardholders: 15%|█▍ | 48/328 [00:14<01:26, 3.23it/s]Matching cardholders: 15%|█▍ | 49/328 [00:15<01:28, 3.15it/s]Matching cardholders: 15%|█▌ | 50/328 [00:15<01:29, 3.12it/s]Matching cardholders: 16%|█▌ | 51/328 [00:15<01:27, 3.16it/s]Matching cardholders: 16%|█▌ | 52/328 [00:16<01:21, 3.39it/s]Matching cardholders: 16%|█▌ | 53/328 [00:16<01:23, 3.28it/s]Matching cardholders: 16%|█▋ | 54/328 [00:16<01:18, 3.48it/s]Matching cardholders: 17%|█▋ | 55/328 [00:17<01:21, 3.36it/s]Matching cardholders: 17%|█▋ | 56/328 [00:17<01:32, 2.93it/s]Matching cardholders: 17%|█▋ | 57/328 [00:17<01:32, 2.92it/s]Matching cardholders: 18%|█▊ | 58/328 [00:18<01:28, 3.06it/s]Matching cardholders: 18%|█▊ | 59/328 [00:18<01:20, 3.35it/s]Matching cardholders: 18%|█▊ | 60/328 [00:18<01:19, 3.38it/s]Matching cardholders: 19%|█▊ | 61/328 [00:18<01:15, 3.55it/s]Matching cardholders: 19%|█▉ | 62/328 [00:19<01:19, 3.35it/s]Matching cardholders: 19%|█▉ | 63/328 [00:19<01:22, 3.22it/s]Matching cardholders: 20%|█▉ | 64/328 [00:19<01:23, 3.16it/s]Matching cardholders: 20%|█▉ | 65/328 [00:20<01:24, 3.11it/s]Matching cardholders: 20%|██ | 66/328 [00:20<01:22, 3.16it/s]Matching cardholders: 20%|██ | 67/328 [00:20<01:24, 3.09it/s]Matching cardholders: 21%|██ | 68/328 [00:21<01:25, 3.04it/s]Matching cardholders: 21%|██ | 69/328 [00:21<01:22, 3.15it/s]Matching cardholders: 21%|██▏ | 70/328 [00:21<01:21, 3.18it/s]Matching cardholders: 22%|██▏ | 71/328 [00:22<01:23, 3.09it/s]Matching cardholders: 22%|██▏ | 72/328 [00:22<01:17, 3.29it/s]Matching cardholders: 22%|██▏ | 73/328 [00:22<01:20, 3.16it/s]Matching cardholders: 23%|██▎ | 74/328 [00:23<01:18, 3.23it/s]Matching cardholders: 23%|██▎ | 75/328 [00:23<01:27, 2.90it/s]Matching cardholders: 23%|██▎ | 76/328 [00:23<01:26, 2.90it/s]Matching cardholders: 23%|██▎ | 77/328 [00:24<01:24, 2.95it/s]Matching cardholders: 24%|██▍ | 78/328 [00:24<01:22, 3.02it/s]Matching cardholders: 24%|██▍ | 79/328 [00:24<01:23, 2.98it/s]Matching cardholders: 24%|██▍ | 80/328 [00:25<01:22, 3.01it/s]Matching cardholders: 25%|██▍ | 81/328 [00:25<01:17, 3.20it/s]Matching cardholders: 25%|██▌ | 82/328 [00:25<01:17, 3.19it/s]Matching cardholders: 25%|██▌ | 83/328 [00:26<01:18, 3.12it/s]Matching cardholders: 26%|██▌ | 84/328 [00:26<01:19, 3.06it/s]Matching cardholders: 26%|██▌ | 85/328 [00:26<01:17, 3.15it/s]Matching cardholders: 26%|██▌ | 86/328 [00:26<01:13, 3.31it/s]Matching cardholders: 27%|██▋ | 87/328 [00:27<01:10, 3.40it/s]Matching cardholders: 27%|██▋ | 88/328 [00:27<01:06, 3.61it/s]Matching cardholders: 27%|██▋ | 89/328 [00:27<01:04, 3.72it/s]Matching cardholders: 27%|██▋ | 90/328 [00:28<01:08, 3.48it/s]Matching cardholders: 28%|██▊ | 91/328 [00:28<01:11, 3.29it/s]Matching cardholders: 28%|██▊ | 92/328 [00:28<01:14, 3.18it/s]Matching cardholders: 28%|██▊ | 93/328 [00:29<01:16, 3.06it/s]Matching cardholders: 29%|██▊ | 94/328 [00:29<01:16, 3.04it/s]Matching cardholders: 29%|██▉ | 95/328 [00:29<01:17, 3.00it/s]Matching cardholders: 29%|██▉ | 96/328 [00:30<01:24, 2.75it/s]Matching cardholders: 30%|██▉ | 97/328 [00:30<01:21, 2.85it/s]Matching cardholders: 30%|██▉ | 98/328 [00:30<01:20, 2.87it/s]Matching cardholders: 30%|███ | 99/328 [00:31<01:18, 2.90it/s]Matching cardholders: 30%|███ | 100/328 [00:31<01:10, 3.22it/s]Matching cardholders: 31%|███ | 101/328 [00:31<01:08, 3.31it/s]Matching cardholders: 31%|███ | 102/328 [00:32<01:11, 3.18it/s]Matching cardholders: 31%|███▏ | 103/328 [00:32<01:12, 3.10it/s]Matching cardholders: 32%|███▏ | 104/328 [00:32<01:13, 3.06it/s]Matching cardholders: 32%|███▏ | 105/328 [00:33<01:10, 3.18it/s]Matching cardholders: 32%|███▏ | 106/328 [00:33<01:11, 3.12it/s]Matching cardholders: 33%|███▎ | 107/328 [00:33<01:10, 3.15it/s]Matching cardholders: 33%|███▎ | 108/328 [00:33<01:04, 3.39it/s]Matching cardholders: 33%|███▎ | 109/328 [00:34<01:01, 3.59it/s]Matching cardholders: 34%|███▎ | 110/328 [00:34<01:04, 3.37it/s]Matching cardholders: 34%|███▍ | 111/328 [00:34<01:07, 3.21it/s]Matching cardholders: 34%|███▍ | 112/328 [00:35<01:07, 3.19it/s]Matching cardholders: 34%|███▍ | 113/328 [00:35<01:05, 3.30it/s]Matching cardholders: 35%|███▍ | 114/328 [00:35<01:07, 3.19it/s]Matching cardholders: 35%|███▌ | 115/328 [00:36<01:08, 3.11it/s]Matching cardholders: 35%|███▌ | 116/328 [00:36<01:06, 3.18it/s]Matching cardholders: 36%|███▌ | 117/328 [00:36<01:07, 3.14it/s]Matching cardholders: 36%|███▌ | 118/328 [00:36<01:02, 3.38it/s]Matching cardholders: 36%|███▋ | 119/328 [00:37<01:11, 2.93it/s]Matching cardholders: 37%|███▋ | 120/328 [00:37<01:07, 3.08it/s]Matching cardholders: 37%|███▋ | 121/328 [00:38<01:08, 3.04it/s]Matching cardholders: 37%|███▋ | 122/328 [00:38<01:03, 3.24it/s]Matching cardholders: 38%|███▊ | 123/328 [00:38<01:02, 3.26it/s]Matching cardholders: 38%|███▊ | 124/328 [00:38<01:02, 3.29it/s]Matching cardholders: 38%|███▊ | 125/328 [00:39<01:03, 3.22it/s]Matching cardholders: 38%|███▊ | 126/328 [00:39<01:04, 3.13it/s]Matching cardholders: 39%|███▊ | 127/328 [00:39<00:59, 3.36it/s]Matching cardholders: 39%|███▉ | 128/328 [00:40<00:57, 3.47it/s]Matching cardholders: 39%|███▉ | 129/328 [00:40<01:00, 3.29it/s]Matching cardholders: 40%|███▉ | 130/328 [00:40<01:00, 3.26it/s]Matching cardholders: 40%|███▉ | 131/328 [00:41<01:02, 3.15it/s]Matching cardholders: 40%|████ | 132/328 [00:41<00:57, 3.39it/s]Matching cardholders: 41%|████ | 133/328 [00:41<01:00, 3.25it/s]Matching cardholders: 41%|████ | 134/328 [00:41<01:01, 3.15it/s]Matching cardholders: 41%|████ | 135/328 [00:42<01:00, 3.21it/s]Matching cardholders: 41%|████▏ | 136/328 [00:42<01:01, 3.12it/s]Matching cardholders: 42%|████▏ | 137/328 [00:42<01:02, 3.05it/s]Matching cardholders: 42%|████▏ | 138/328 [00:43<01:03, 3.01it/s]Matching cardholders: 42%|████▏ | 139/328 [00:43<01:03, 2.98it/s]Matching cardholders: 43%|████▎ | 140/328 [00:43<01:02, 3.01it/s]Matching cardholders: 43%|████▎ | 141/328 [00:44<00:57, 3.23it/s]Matching cardholders: 43%|████▎ | 142/328 [00:44<01:02, 2.98it/s]Matching cardholders: 44%|████▎ | 143/328 [00:44<01:02, 2.96it/s]Matching cardholders: 44%|████▍ | 144/328 [00:45<01:02, 2.95it/s]Matching cardholders: 44%|████▍ | 145/328 [00:45<00:59, 3.09it/s]Matching cardholders: 45%|████▍ | 146/328 [00:45<00:59, 3.08it/s]Matching cardholders: 45%|████▍ | 147/328 [00:46<00:59, 3.04it/s]Matching cardholders: 45%|████▌ | 148/328 [00:46<00:56, 3.17it/s]Matching cardholders: 45%|████▌ | 149/328 [00:46<00:55, 3.25it/s]Matching cardholders: 46%|████▌ | 150/328 [00:47<00:56, 3.14it/s]Matching cardholders: 46%|████▌ | 151/328 [00:47<00:53, 3.29it/s]Matching cardholders: 46%|████▋ | 152/328 [00:47<00:55, 3.19it/s]Matching cardholders: 47%|████▋ | 153/328 [00:48<00:52, 3.33it/s]Matching cardholders: 47%|████▋ | 154/328 [00:48<00:53, 3.28it/s]Matching cardholders: 47%|████▋ | 155/328 [00:48<00:54, 3.17it/s]Matching cardholders: 48%|████▊ | 156/328 [00:49<00:55, 3.09it/s]Matching cardholders: 48%|████▊ | 157/328 [00:49<00:55, 3.06it/s]Matching cardholders: 48%|████▊ | 158/328 [00:49<00:54, 3.09it/s]Matching cardholders: 48%|████▊ | 159/328 [00:50<00:53, 3.18it/s]Matching cardholders: 49%|████▉ | 160/328 [00:50<00:51, 3.26it/s]Matching cardholders: 49%|████▉ | 161/328 [00:50<00:49, 3.36it/s]Matching cardholders: 49%|████▉ | 162/328 [00:50<00:49, 3.33it/s]Matching cardholders: 50%|████▉ | 163/328 [00:51<00:48, 3.41it/s]Matching cardholders: 50%|█████ | 164/328 [00:51<00:51, 3.19it/s]Matching cardholders: 50%|█████ | 165/328 [00:51<00:58, 2.76it/s]Matching cardholders: 51%|█████ | 166/328 [00:52<00:57, 2.81it/s]Matching cardholders: 51%|█████ | 167/328 [00:52<00:55, 2.91it/s]Matching cardholders: 51%|█████ | 168/328 [00:52<00:54, 2.94it/s]Matching cardholders: 52%|█████▏ | 169/328 [00:53<00:54, 2.93it/s]Matching cardholders: 52%|█████▏ | 170/328 [00:53<00:53, 2.96it/s]Matching cardholders: 52%|█████▏ | 171/328 [00:53<00:52, 2.98it/s]Matching cardholders: 52%|█████▏ | 172/328 [00:54<00:52, 2.98it/s]Matching cardholders: 53%|█████▎ | 173/328 [00:54<00:49, 3.16it/s]Matching cardholders: 53%|█████▎ | 174/328 [00:54<00:47, 3.28it/s]Matching cardholders: 53%|█████▎ | 175/328 [00:55<00:48, 3.16it/s]Matching cardholders: 54%|█████▎ | 176/328 [00:55<00:49, 3.08it/s]Matching cardholders: 54%|█████▍ | 177/328 [00:55<00:50, 2.99it/s]Matching cardholders: 54%|█████▍ | 178/328 [00:56<00:50, 2.98it/s]Matching cardholders: 55%|█████▍ | 179/328 [00:56<00:49, 2.98it/s]Matching cardholders: 55%|█████▍ | 180/328 [00:56<00:50, 2.94it/s]Matching cardholders: 55%|█████▌ | 181/328 [00:57<00:47, 3.08it/s]Matching cardholders: 55%|█████▌ | 182/328 [00:57<00:46, 3.13it/s]Matching cardholders: 56%|█████▌ | 183/328 [00:57<00:48, 2.98it/s]Matching cardholders: 56%|█████▌ | 184/328 [00:58<00:51, 2.80it/s]Matching cardholders: 56%|█████▋ | 185/328 [00:58<00:49, 2.87it/s]Matching cardholders: 57%|█████▋ | 186/328 [00:58<00:48, 2.94it/s]Matching cardholders: 57%|█████▋ | 187/328 [00:59<00:48, 2.93it/s]Matching cardholders: 57%|█████▋ | 188/328 [00:59<00:52, 2.69it/s]Matching cardholders: 58%|█████▊ | 189/328 [01:00<00:48, 2.87it/s]Matching cardholders: 58%|█████▊ | 190/328 [01:00<00:47, 2.89it/s]Matching cardholders: 58%|█████▊ | 191/328 [01:00<00:46, 2.92it/s]Matching cardholders: 59%|█████▊ | 192/328 [01:01<00:45, 3.02it/s]Matching cardholders: 59%|█████▉ | 193/328 [01:01<00:43, 3.07it/s]Matching cardholders: 59%|█████▉ | 194/328 [01:01<00:42, 3.13it/s]Matching cardholders: 59%|█████▉ | 195/328 [01:01<00:43, 3.07it/s]Matching cardholders: 60%|█████▉ | 196/328 [01:02<00:42, 3.09it/s]Matching cardholders: 60%|██████ | 197/328 [01:02<00:41, 3.13it/s]Matching cardholders: 60%|██████ | 198/328 [01:02<00:42, 3.07it/s]Matching cardholders: 61%|██████ | 199/328 [01:03<00:41, 3.12it/s]Matching cardholders: 61%|██████ | 200/328 [01:03<00:41, 3.08it/s]Matching cardholders: 61%|██████▏ | 201/328 [01:03<00:41, 3.03it/s]Matching cardholders: 62%|██████▏ | 202/328 [01:04<00:41, 3.06it/s]Matching cardholders: 62%|██████▏ | 203/328 [01:04<00:41, 3.02it/s]Matching cardholders: 62%|██████▏ | 204/328 [01:04<00:41, 2.99it/s]Matching cardholders: 62%|██████▎ | 205/328 [01:05<00:41, 2.96it/s]Matching cardholders: 63%|██████▎ | 206/328 [01:05<00:41, 2.95it/s]Matching cardholders: 63%|██████▎ | 207/328 [01:05<00:41, 2.95it/s]Matching cardholders: 63%|██████▎ | 208/328 [01:06<00:39, 3.03it/s]Matching cardholders: 64%|██████▎ | 209/328 [01:06<00:39, 3.00it/s]Matching cardholders: 64%|██████▍ | 210/328 [01:06<00:39, 2.98it/s]Matching cardholders: 64%|██████▍ | 211/328 [01:07<00:43, 2.71it/s]Matching cardholders: 65%|██████▍ | 212/328 [01:07<00:40, 2.83it/s]Matching cardholders: 65%|██████▍ | 213/328 [01:08<00:40, 2.87it/s]Matching cardholders: 65%|██████▌ | 214/328 [01:08<00:39, 2.87it/s]Matching cardholders: 66%|██████▌ | 215/328 [01:08<00:39, 2.89it/s]Matching cardholders: 66%|██████▌ | 216/328 [01:09<00:38, 2.91it/s]Matching cardholders: 66%|██████▌ | 217/328 [01:09<00:38, 2.90it/s]Matching cardholders: 66%|██████▋ | 218/328 [01:09<00:37, 2.92it/s]Matching cardholders: 67%|██████▋ | 219/328 [01:10<00:36, 2.97it/s]Matching cardholders: 67%|██████▋ | 220/328 [01:10<00:36, 2.95it/s]Matching cardholders: 67%|██████▋ | 221/328 [01:10<00:36, 2.94it/s]Matching cardholders: 68%|██████▊ | 222/328 [01:11<00:35, 3.00it/s]Matching cardholders: 68%|██████▊ | 223/328 [01:11<00:34, 3.04it/s]Matching cardholders: 68%|██████▊ | 224/328 [01:11<00:34, 3.00it/s]Matching cardholders: 69%|██████▊ | 225/328 [01:12<00:35, 2.93it/s]Matching cardholders: 69%|██████▉ | 226/328 [01:12<00:34, 2.92it/s]Matching cardholders: 69%|██████▉ | 227/328 [01:12<00:34, 2.92it/s]Matching cardholders: 70%|██████▉ | 228/328 [01:13<00:34, 2.92it/s]Matching cardholders: 70%|██████▉ | 229/328 [01:13<00:33, 2.97it/s]Matching cardholders: 70%|███████ | 230/328 [01:13<00:33, 2.95it/s]Matching cardholders: 70%|███████ | 231/328 [01:14<00:32, 2.94it/s]Matching cardholders: 71%|███████ | 232/328 [01:14<00:32, 2.93it/s]Matching cardholders: 71%|███████ | 233/328 [01:14<00:32, 2.97it/s]Matching cardholders: 71%|███████▏ | 234/328 [01:15<00:34, 2.71it/s]Matching cardholders: 72%|███████▏ | 235/328 [01:15<00:33, 2.77it/s]Matching cardholders: 72%|███████▏ | 236/328 [01:15<00:32, 2.81it/s]Matching cardholders: 72%|███████▏ | 237/328 [01:16<00:32, 2.82it/s]Matching cardholders: 73%|███████▎ | 238/328 [01:16<00:31, 2.86it/s]Matching cardholders: 73%|███████▎ | 239/328 [01:16<00:30, 2.87it/s]Matching cardholders: 73%|███████▎ | 240/328 [01:17<00:30, 2.88it/s]Matching cardholders: 73%|███████▎ | 241/328 [01:17<00:30, 2.90it/s]Matching cardholders: 74%|███████▍ | 242/328 [01:18<00:29, 2.90it/s]Matching cardholders: 74%|███████▍ | 243/328 [01:18<00:29, 2.90it/s]Matching cardholders: 74%|███████▍ | 244/328 [01:18<00:28, 2.92it/s]Matching cardholders: 75%|███████▍ | 245/328 [01:19<00:28, 2.92it/s]Matching cardholders: 75%|███████▌ | 246/328 [01:19<00:28, 2.92it/s]Matching cardholders: 75%|███████▌ | 247/328 [01:19<00:27, 2.91it/s]Matching cardholders: 76%|███████▌ | 248/328 [01:20<00:27, 2.92it/s]Matching cardholders: 76%|███████▌ | 249/328 [01:20<00:26, 2.93it/s]Matching cardholders: 76%|███████▌ | 250/328 [01:20<00:26, 2.93it/s]Matching cardholders: 77%|███████▋ | 251/328 [01:21<00:26, 2.93it/s]Matching cardholders: 77%|███████▋ | 252/328 [01:21<00:25, 2.92it/s]Matching cardholders: 77%|███████▋ | 253/328 [01:21<00:25, 2.93it/s]Matching cardholders: 77%|███████▋ | 254/328 [01:22<00:25, 2.91it/s]Matching cardholders: 78%|███████▊ | 255/328 [01:22<00:25, 2.91it/s]Matching cardholders: 78%|███████▊ | 256/328 [01:22<00:24, 2.92it/s]Matching cardholders: 78%|███████▊ | 257/328 [01:23<00:26, 2.68it/s]Matching cardholders: 79%|███████▊ | 258/328 [01:23<00:25, 2.74it/s]Matching cardholders: 79%|███████▉ | 259/328 [01:23<00:24, 2.80it/s]Matching cardholders: 79%|███████▉ | 260/328 [01:24<00:23, 2.84it/s]Matching cardholders: 80%|███████▉ | 261/328 [01:24<00:23, 2.86it/s]Matching cardholders: 80%|███████▉ | 262/328 [01:24<00:22, 2.88it/s]Matching cardholders: 80%|████████ | 263/328 [01:25<00:22, 2.90it/s]Matching cardholders: 80%|████████ | 264/328 [01:25<00:22, 2.90it/s]Matching cardholders: 81%|████████ | 265/328 [01:25<00:21, 2.91it/s]Matching cardholders: 81%|████████ | 266/328 [01:26<00:21, 2.92it/s]Matching cardholders: 81%|████████▏ | 267/328 [01:26<00:20, 2.91it/s]Matching cardholders: 82%|████████▏ | 268/328 [01:27<00:20, 2.92it/s]Matching cardholders: 82%|████████▏ | 269/328 [01:27<00:20, 2.93it/s]Matching cardholders: 82%|████████▏ | 270/328 [01:27<00:19, 2.91it/s]Matching cardholders: 83%|████████▎ | 271/328 [01:28<00:19, 2.91it/s]Matching cardholders: 83%|████████▎ | 272/328 [01:28<00:19, 2.91it/s]Matching cardholders: 83%|████████▎ | 273/328 [01:28<00:19, 2.82it/s]Matching cardholders: 84%|████████▎ | 274/328 [01:29<00:18, 2.85it/s]Matching cardholders: 84%|████████▍ | 275/328 [01:29<00:18, 2.87it/s]Matching cardholders: 84%|████████▍ | 276/328 [01:29<00:18, 2.89it/s]Matching cardholders: 84%|████████▍ | 277/328 [01:30<00:17, 2.90it/s]Matching cardholders: 85%|████████▍ | 278/328 [01:30<00:17, 2.90it/s]Matching cardholders: 85%|████████▌ | 279/328 [01:30<00:16, 2.90it/s]Matching cardholders: 85%|████████▌ | 280/328 [01:31<00:18, 2.66it/s]Matching cardholders: 86%|████████▌ | 281/328 [01:31<00:17, 2.73it/s]Matching cardholders: 86%|████████▌ | 282/328 [01:31<00:16, 2.79it/s]Matching cardholders: 86%|████████▋ | 283/328 [01:32<00:15, 2.83it/s]Matching cardholders: 87%|████████▋ | 284/328 [01:32<00:15, 2.86it/s]Matching cardholders: 87%|████████▋ | 285/328 [01:33<00:14, 2.87it/s]Matching cardholders: 87%|████████▋ | 286/328 [01:33<00:14, 2.90it/s]Matching cardholders: 88%|████████▊ | 287/328 [01:33<00:14, 2.90it/s]Matching cardholders: 88%|████████▊ | 288/328 [01:34<00:13, 2.91it/s]Matching cardholders: 88%|████████▊ | 289/328 [01:34<00:13, 2.90it/s]Matching cardholders: 88%|████████▊ | 290/328 [01:34<00:13, 2.91it/s]Matching cardholders: 89%|████████▊ | 291/328 [01:35<00:12, 2.91it/s]Matching cardholders: 89%|████████▉ | 292/328 [01:35<00:12, 2.92it/s]Matching cardholders: 89%|████████▉ | 293/328 [01:35<00:12, 2.91it/s]Matching cardholders: 90%|████████▉ | 294/328 [01:36<00:11, 2.91it/s]Matching cardholders: 90%|████████▉ | 295/328 [01:36<00:11, 2.92it/s]Matching cardholders: 90%|█████████ | 296/328 [01:36<00:10, 2.91it/s]Matching cardholders: 91%|█████████ | 297/328 [01:37<00:10, 2.93it/s]Matching cardholders: 91%|█████████ | 298/328 [01:37<00:10, 2.92it/s]Matching cardholders: 91%|█████████ | 299/328 [01:37<00:09, 2.91it/s]Matching cardholders: 91%|█████████▏| 300/328 [01:38<00:09, 2.92it/s]Matching cardholders: 92%|█████████▏| 301/328 [01:38<00:09, 2.92it/s]Matching cardholders: 92%|█████████▏| 302/328 [01:38<00:08, 2.92it/s]Matching cardholders: 92%|█████████▏| 303/328 [01:39<00:09, 2.68it/s]Matching cardholders: 93%|█████████▎| 304/328 [01:39<00:08, 2.75it/s]Matching cardholders: 93%|█████████▎| 305/328 [01:39<00:08, 2.81it/s]Matching cardholders: 93%|█████████▎| 306/328 [01:40<00:07, 2.84it/s]Matching cardholders: 94%|█████████▎| 307/328 [01:40<00:07, 2.86it/s]Matching cardholders: 94%|█████████▍| 308/328 [01:40<00:06, 2.89it/s]Matching cardholders: 94%|█████████▍| 309/328 [01:41<00:06, 2.90it/s]Matching cardholders: 95%|█████████▍| 310/328 [01:41<00:06, 2.90it/s]Matching cardholders: 95%|█████████▍| 311/328 [01:41<00:05, 2.91it/s]Matching cardholders: 95%|█████████▌| 312/328 [01:42<00:05, 2.92it/s]Matching cardholders: 95%|█████████▌| 313/328 [01:42<00:05, 2.91it/s]Matching cardholders: 96%|█████████▌| 314/328 [01:43<00:04, 2.92it/s]Matching cardholders: 96%|█████████▌| 315/328 [01:43<00:04, 2.92it/s]Matching cardholders: 96%|█████████▋| 316/328 [01:43<00:04, 2.92it/s]Matching cardholders: 97%|█████████▋| 317/328 [01:44<00:03, 2.92it/s]Matching cardholders: 97%|█████████▋| 318/328 [01:44<00:03, 2.92it/s]Matching cardholders: 97%|█████████▋| 319/328 [01:44<00:03, 2.91it/s]Matching cardholders: 98%|█████████▊| 320/328 [01:45<00:02, 2.92it/s]Matching cardholders: 98%|█████████▊| 321/328 [01:45<00:02, 2.90it/s]Matching cardholders: 98%|█████████▊| 322/328 [01:45<00:02, 2.84it/s]Matching cardholders: 98%|█████████▊| 323/328 [01:46<00:01, 2.86it/s]Matching cardholders: 99%|█████████▉| 324/328 [01:46<00:01, 2.89it/s]Matching cardholders: 99%|█████████▉| 325/328 [01:46<00:01, 2.89it/s]Matching cardholders: 99%|█████████▉| 326/328 [01:47<00:00, 2.65it/s]Matching cardholders: 100%|█████████▉| 327/328 [01:47<00:00, 2.73it/s]Matching cardholders: 100%|██████████| 328/328 [01:47<00:00, 2.78it/s]Matching cardholders: 100%|██████████| 328/328 [01:47<00:00, 3.04it/s]
Percentage of clients with card issued: 8.33%
Percentage of clients with card issued after matching: 16.67%
After each non-cardholder got the artifical card issued date assigned we drop the remaining non-cardholders without a match.
before_len = len(matched_non_card_holders_w_issue_date_df)
print(-(before_len - len(matched_non_card_holders_w_issue_date_df)))
matched_non_card_holders_w_issue_date_df = (
matched_non_card_holders_w_issue_date_df.dropna(subset=["card_issued"])
)
data_reduction["Non-cardholders without match"] = -(
before_len - len(matched_non_card_holders_w_issue_date_df)
)
del before_len0
4.3 Aggregate on a Monthly Basis
After matching cardholders with non-cardholders and setting artificial card issue dates, we aggregate the transactional data on a monthly basis. This aggregation provides a comprehensive overview of financial activities for each account, facilitating further model development providing us with a fixed of features to work with.
The function aggregate_transactions_monthly is designed to process and summarize financial transactions on a monthly basis for each account within a dataset. The explanation of its workings, step by step, is as follows:
Sorting Transactions: Initially, the function sorts the transactions in the provided DataFrame
transactions_dfbased onaccount_idand the transactiondate. This ensures that all transactions for a given account are ordered chronologically, which is crucial for accurate monthly aggregation and cumulative balance calculation.Monthly Grouping: Each transaction’s date is then converted to a monthly period using
dt.to_period("M"). This step categorizes each transaction by the month and year it occurred, facilitating the aggregation of transactions on a monthly basis.Aggregation of Monthly Data: The function groups the sorted transactions by
account_idand the newly createdmonthcolumn. For each group, it calculates several metrics:volume: The sum of all transactions’ amounts for the month, representing the total money flow.total_abs_amount: The sum of the absolute values of the transactions’ amounts, indicating the total amount of money moved, disregarding the direction.transaction_count: The count of transactions, providing a sense of activity level.positive_transaction_countandnegative_transaction_count: The counts of positive (inflows) and negative (outflows) transactions, respectively. This distinction can help identify the balance between income and expenses.- Statistical measures like
average_amount,median_amount,min_amount,max_amount, andstd_amountoffer insights into the distribution of transaction amounts. type_count,operation_count, andk_symbol_count: The counts of unique transaction types, operations, and transaction symbols (k_symbol), respectively, indicating the diversity of transaction characteristics.
Cumulative Balance Calculation: After aggregating the monthly data, the function computes a cumulative balance (
balance) for each account by cumulatively summing thevolume(total transaction amount) over time. This step provides insight into how the account balance evolves over the months.
As we have already explored and verified in the EDA section of the transactional data, each account starts with a transaction where the amount equals the inital balance. This validation ensures the integrity of the aggregated data, as the balance should accurately reflect the total transaction volume over time.
def aggregate_transactions_monthly(df):
"""
Aggregate financial transaction data on a monthly basis per account.
Parameters:
- df (pd.DataFrame): DataFrame containing financial transaction data with 'account_id', 'date', and other relevant columns.
- validate (bool): If True, validate the aggregated data. Default is True.
Returns:
- pd.DataFrame: Monthly aggregated financial transaction data per account.
"""
df_sorted = df.sort_values(by=["account_id", "date"])
df_sorted["month"] = df_sorted["date"].dt.to_period("M")
monthly_aggregated_data = (
df_sorted.groupby(["account_id", "month"])
.agg(
volume=("amount", "sum"),
total_abs_amount=("amount", lambda x: x.abs().sum()),
transaction_count=("amount", "count"),
positive_transaction_count=(
"amount",
lambda x: (x >= 0).sum(),
), # TODO: it seems that there are some transactions with 0 amount, how to handle those?
negative_transaction_count=("amount", lambda x: (x < 0).sum()),
average_amount=("amount", "mean"),
median_amount=("amount", "median"),
min_amount=("amount", "min"),
max_amount=("amount", "max"),
std_amount=("amount", "std"),
type_count=("transaction_type", "nunique"),
operation_count=("operation", "nunique"),
k_symbol_count=("k_symbol", "nunique"),
)
.reset_index()
.sort_values(by=["account_id", "month"])
)
monthly_aggregated_data["balance"] = monthly_aggregated_data.groupby("account_id")[
"volume"
].cumsum()
return monthly_aggregated_data
agg_transactions_monthly_df = aggregate_transactions_monthly(transactions_df)
agg_transactions_monthly_df.to_csv("./data/agg_transactions_monthly.csv", index=False)
agg_transactions_monthly_df.describe()| account_id | volume | total_abs_amount | transaction_count | positive_transaction_count | negative_transaction_count | average_amount | median_amount | min_amount | max_amount | std_amount | type_count | operation_count | k_symbol_count | balance | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 185057.000000 | 185057.000000 | 185057.000000 | 185057.000000 | 185057.000000 | 185057.000000 | 185057.000000 | 185057.000000 | 185057.000000 | 185057.000000 | 176803.000000 | 185057.000000 | 185057.000000 | 185057.000000 | 185057.000000 |
| mean | 2799.983292 | 1065.354397 | 33815.492309 | 5.708079 | 2.189017 | 3.519062 | 451.659265 | -372.421445 | -9607.378249 | 14756.009580 | 9030.305445 | 1.921181 | 3.568965 | 3.719649 | 34474.787632 |
| std | 2331.861909 | 12509.136299 | 37724.985550 | 2.417842 | 0.726115 | 2.173427 | 2479.100575 | 1933.445907 | 10746.883348 | 12958.692736 | 7402.806514 | 0.269457 | 0.832363 | 1.085701 | 19799.443508 |
| min | 1.000000 | -101550.300000 | 14.600000 | 1.000000 | 0.000000 | 0.000000 | -37000.000000 | -37000.000000 | -87400.000000 | -37000.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | -41125.800000 |
| 25% | 1172.000000 | -2266.600000 | 9659.500000 | 4.000000 | 2.000000 | 2.000000 | -379.566667 | -785.000000 | -13428.000000 | 4756.000000 | 3283.937059 | 2.000000 | 3.000000 | 3.000000 | 20405.600000 |
| 50% | 2375.000000 | 1058.100000 | 22933.100000 | 5.000000 | 2.000000 | 3.000000 | 220.260000 | -14.600000 | -6177.000000 | 10929.000000 | 6824.369949 | 2.000000 | 4.000000 | 4.000000 | 30000.000000 |
| 75% | 3576.000000 | 4132.200000 | 43668.000000 | 7.000000 | 2.000000 | 5.000000 | 878.680000 | 44.700000 | -2672.000000 | 21553.000000 | 12622.945077 | 2.000000 | 4.000000 | 4.000000 | 44540.500000 |
| max | 11382.000000 | 115038.200000 | 609736.200000 | 23.000000 | 9.000000 | 16.000000 | 44708.000000 | 44708.000000 | 44708.000000 | 74812.000000 | 57782.701468 | 2.000000 | 6.000000 | 7.000000 | 138317.800000 |
The validate_monthly_aggregated_transactions function is invoked to ensure the integrity and correctness of the aggregated data through several assertions:
- The balance should consistently increase or decrease based on whether the total monthly transaction volume is positive or negative, respectively.
- For each account, the balance in the first month should equal the total transaction volume of that month.
- The sum of positive and negative transaction counts must equal the total transaction count for each month.
- The number of unique accounts in the aggregated data should match that in the original dataset.
- The final balances of accounts in the aggregated data should closely match their last recorded transactions in the original dataset.
def validate_monthly_aggregated_transactions(aggregated_data, original_df):
"""
Validate the integrity and correctness of aggregated monthly financial transactions.
Parameters:
- aggregated_data (pd.DataFrame): Aggregated monthly transaction data.
- original_df (pd.DataFrame): Original dataset of financial transactions.
Raises:
- AssertionError: If validation conditions are not met.
"""
assert (aggregated_data["volume"] >= 0).all() == (
aggregated_data["balance"].diff() >= 0
).all(), "If the total amount is positive, the balance should go up."
assert (aggregated_data["volume"] < 0).all() == (
aggregated_data["balance"].diff() < 0
).all(), "If the total amount is negative, the balance should go down."
first_month = aggregated_data.groupby("account_id").nth(0)
assert (
first_month["volume"] == first_month["balance"]
).all(), "The balance should equal the volume for the first month."
assert (
aggregated_data["positive_transaction_count"]
+ aggregated_data["negative_transaction_count"]
== aggregated_data["transaction_count"]
).all(), "The sum of positive and negative transaction counts should equal the total transaction count."
assert (
aggregated_data["account_id"].nunique() == original_df["account_id"].nunique()
), "The number of unique account_ids in the aggregated DataFrame should be the same as the original DataFrame."
assert (
pd.merge(
aggregated_data.groupby("account_id")
.last()
.reset_index()[["account_id", "balance"]],
original_df[
original_df.groupby("account_id")["date"].transform("max")
== original_df["date"]
][["account_id", "balance"]],
on="account_id",
suffixes=("_final", "_last"),
)
.apply(
lambda x: np.isclose(x["balance_final"], x["balance_last"], atol=5), axis=1
)
.any()
), "Some accounts' final balances do not match their last transactions."
validate_monthly_aggregated_transactions(agg_transactions_monthly_df, transactions_df)5 Exploratory Data Analysis: Aggregated Monthly Transactions
5.1 Monthly Balance Difference and Volume
This plot gives a clear picture of how money moves in and out of an account each month and how these movements affect the overall balance. It does this by showing two things:
- Balance Difference: This line shows whether the account balance went up or down each month. If the line goes up, it means the account gained money that month. If it goes down, the account lost money.
- Volume: This line shows the total amount of money that moved in the account each month, regardless of whether it was coming in or going out.
What to Look For: - A direct link between the amount of money moved (volume) and changes in the account balance. High incoming money should lead to an uptick in the balance, and lots of outgoing money should lead to a downturn. - This visual check helps to understand how active the account is and whether it’s generally getting fuller or emptier over time.
def plot_monthly_balance_diff_and_volume(
transactions_monthly, account_id
):
account_transactions = transactions_monthly[
transactions_monthly["account_id"] == account_id
].sort_values(by="month")
account_transactions["balance_diff"] = account_transactions["balance"].diff()
plt.figure(figsize=(9.5, 6))
plt.plot(
account_transactions["month"].astype(str),
account_transactions["balance_diff"],
marker="o",
label="Balance Difference",
)
plt.plot(
account_transactions["month"].astype(str),
account_transactions["volume"],
marker="x",
linestyle="--",
label="Volume",
)
plt.title(f"Monthly Balance Difference and Volume for Account {account_id}")
plt.xlabel("Month")
plt.ylabel("Value")
plt.xticks(rotation=90, fontsize=7)
plt.yticks(fontsize=8)
plt.legend()
plt.grid(True)
plt.show()
plot_monthly_balance_diff_and_volume(agg_transactions_monthly_df, 2)5.2 Monthly Transactions, Balance, and Volume Plot Explanation
This visualization offers a snapshot of an account’s activity over time by comparing money movement each month with the overall account balance. It helps to understand:
- Volume: How much money came in or went out of the account each month. Incoming money is shown as up, and outgoing money as down.
- Balance: The total money in the account at the end of each month, showing how it’s changed over time due to the monthly transactions.
What to Look For: - How the monthly money movement impacts the account’s growing or shrinking balance. For example, a few months of high income should visibly increase the balance. - This simple visual guide helps spot trends, like if the account is steadily growing, holding steady, or facing issues, giving quick insights into financial well-being and further validates the aggregation made in the previous step.
def plot_monthly_transactions_balance_and_volume(agg_transactions_monthly, account_id):
account_transactions = agg_transactions_monthly[
agg_transactions_monthly["account_id"] == account_id
]
plt.figure(figsize=(9.5, 6))
plt.plot(
account_transactions["month"].astype(str),
account_transactions["volume"],
marker="o",
label="Volume",
)
plt.plot(
account_transactions["month"].astype(str),
account_transactions["balance"],
marker="x",
linestyle="--",
label="Balance",
)
plt.title(f"Monthly Transactions and Balance for Account {account_id}")
plt.xlabel("Month")
plt.ylabel("Value")
plt.xticks(rotation=90, fontsize=7)
plt.yticks(fontsize=8)
plt.legend()
plt.grid(True)
plt.show()
plot_monthly_transactions_balance_and_volume(agg_transactions_monthly_df, 2)5.3 Delieverable: Closer Look at Account 14
plot_monthly_transactions_balance_and_volume(agg_transactions_monthly_df, 14)Account 14 shows a rather conservative transaction history. The spending habits are all withing range of 10k to -10k per month. We can see little volatility, the account shows a slight trend of growing.
5.4 Delieverable: Closer Look at Account 18
plot_monthly_transactions_balance_and_volume(agg_transactions_monthly_df, 18)Account 18 paints a different picture in comparison to account 14.
The volatility here is a lot higher, indiciating a potential for a business account or high income household. Especially March 1994 to December 1994 show some volatile transaction habits.
Looking at the balance and volume per month for the accounts 14 and 18 we can notice some interesting patterns.
TODO: Add analysis
6 Pivot Transactions: Rolling Up to Monthly Aggregates
We have condensed transaction data into a monthly aggregated format. This aggregation serves a multifaceted purpose:
- Monthly aggregation standardizes the time frame across which we analyze transactions, allowing us to compare transactional behaviors consistently across all accounts.
- Aggregating data on a monthly level illuminates patterns that daily data might obscure. It enables us to discern trends over a broader time scale, capturing cyclical behaviors, seasonal effects, and response to macroeconomic events.
- Daily transaction data can be “noisy” with random fluctuations. By considering monthly totals and averages, we reduce this noise, revealing underlying trends more clearly.
- Our primary objective is to understand behaviors leading up to the issuance of a card. Aggregating transactions on a monthly basis helps focus on the crucial period preceding card issuance, enabling us to correlate transactional behaviors with the propensity to become a cardholder.
def pivot_transactions(
non_transactional, transactions_monthly, months_before_card_range=(2, 13)
):
"""
Aggregate monthly transaction data and merge it with non-transactional account data,
focusing on the time frame leading up to the card issuance.
This function merges monthly transaction data with non-transactional data to associate each
transaction with the respective account and card issued date. It then filters transactions based
on a specified range of months before card issuance and aggregates various transaction metrics.
Parameters:
- non_transactional (pd.DataFrame): A DataFrame containing non-transactional account data. This is only used to map card issuance dates to transactions.
- transactions_monthly (pd.DataFrame): A DataFrame containing monthly transaction data.
- months_before_card_range (tuple): A tuple specifying the inclusive range of months before card
issuance to filter the transactions for aggregation.
The aggregation includes the sum of volume and transaction counts, as well as the mean and other
statistical measures of transaction amounts, for each account within the specified months before
card issuance.
The resulting DataFrame is pivoted to have 'account_id' as rows and the months before card
issuance as columns, with aggregated metrics as values. Column names are constructed to
describe the month and the metric represented.
Returns:
- pd.DataFrame: The final aggregated and pivoted dataset ready for analysis, with each row
representing an account and each column a specific metric in the months before
card issuance.
"""
merged_df = transactions_monthly.merge(
non_transactional[["account_id"]], on="account_id"
)
merged_df["card_issued_date"] = merged_df["account_id"].map(
non_transactional.set_index("account_id")["card_issued"]
)
merged_df["months_before_card"] = merged_df.apply(
lambda row: (row["card_issued_date"].to_period("M") - row["month"]).n, axis=1
)
start_month, end_month = months_before_card_range
filtered_df = merged_df.query(f"{start_month} <= months_before_card <= {end_month}")
aggregated_data = (
filtered_df.groupby(["account_id", "months_before_card"])
.agg(
{
"volume": "sum",
"total_abs_amount": "sum",
"transaction_count": "sum",
"positive_transaction_count": "sum",
"negative_transaction_count": "sum",
"average_amount": "mean",
"median_amount": "median",
"min_amount": "min",
"max_amount": "max",
"std_amount": "std",
"type_count": "sum",
"operation_count": "sum",
"k_symbol_count": "sum",
"balance": "mean",
}
)
.reset_index()
)
pivoted_data = aggregated_data.pivot(
index="account_id", columns="months_before_card"
)
pivoted_data.columns = [
"_".join(["M", str(col[1]), col[0]]) for col in pivoted_data.columns.values
]
final_dataset = pivoted_data.reset_index()
return final_dataset
transactions_pivoted_df = pivot_transactions(
matched_non_card_holders_w_issue_date_df, agg_transactions_monthly_df
)
transactions_pivoted_df.describe()| account_id | M_2_volume | M_3_volume | M_4_volume | M_5_volume | M_6_volume | M_7_volume | M_8_volume | M_9_volume | M_10_volume | ... | M_4_balance | M_5_balance | M_6_balance | M_7_balance | M_8_balance | M_9_balance | M_10_balance | M_11_balance | M_12_balance | M_13_balance | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 656.000000 | 656.000000 | 656.000000 | 656.000000 | 656.000000 | 656.000000 | 656.000000 | 656.000000 | 656.000000 | 656.000000 | ... | 656.000000 | 656.000000 | 656.000000 | 656.000000 | 656.000000 | 656.000000 | 656.000000 | 655.000000 | 656.000000 | 656.000000 |
| mean | 2824.231707 | -188.081402 | 2131.023476 | -83.378354 | 1166.012652 | -662.276982 | 1418.670732 | 1039.067683 | 1139.186890 | -633.459299 | ... | 43713.226677 | 43796.605030 | 42630.592378 | 43292.869360 | 41874.198628 | 40835.130945 | 39695.944055 | 40352.773588 | 39736.136585 | 39596.693293 |
| std | 2370.634460 | 12069.931426 | 13667.115448 | 13070.792019 | 14518.623820 | 13857.280237 | 14971.381727 | 13142.470175 | 12976.711696 | 15761.581790 | ... | 20210.049464 | 21178.364095 | 20094.254246 | 20971.757649 | 20486.289111 | 19711.803414 | 19561.774856 | 20190.800543 | 19130.114948 | 19766.034091 |
| min | 11.000000 | -76779.500000 | -54322.500000 | -69155.200000 | -62718.000000 | -67190.700000 | -62113.900000 | -84970.900000 | -75013.500000 | -66945.600000 | ... | 1762.200000 | -4575.900000 | 677.800000 | -8789.700000 | -1299.300000 | -7.900000 | -3269.700000 | 820.700000 | -9843.200000 | 192.000000 |
| 25% | 1146.750000 | -2860.275000 | -2118.800000 | -2363.675000 | -3044.425000 | -3524.050000 | -2881.525000 | -1730.000000 | -2057.200000 | -3582.750000 | ... | 26655.925000 | 26337.725000 | 26830.275000 | 26528.400000 | 25485.675000 | 25222.525000 | 24919.950000 | 24950.750000 | 25026.475000 | 24524.575000 |
| 50% | 2330.500000 | 601.850000 | 1345.850000 | 1163.600000 | 1196.400000 | 841.700000 | 1198.750000 | 1328.200000 | 1105.650000 | 890.900000 | ... | 42680.500000 | 42269.700000 | 41417.900000 | 41245.050000 | 39681.050000 | 38903.600000 | 36803.850000 | 37070.200000 | 37223.300000 | 36958.000000 |
| 75% | 3666.000000 | 3583.525000 | 4677.725000 | 4250.250000 | 4512.375000 | 4456.775000 | 4391.850000 | 4530.200000 | 4713.650000 | 3944.450000 | ... | 54942.825000 | 55514.200000 | 54229.400000 | 56922.150000 | 53359.175000 | 53476.650000 | 51744.600000 | 50787.050000 | 50391.900000 | 49328.125000 |
| max | 11382.000000 | 57541.800000 | 76136.000000 | 60272.100000 | 69456.400000 | 65912.700000 | 88209.200000 | 55601.500000 | 72059.700000 | 98041.500000 | ... | 105453.800000 | 112335.700000 | 106459.000000 | 111264.300000 | 106472.800000 | 108358.900000 | 132286.300000 | 108202.400000 | 112159.300000 | 111050.900000 |
8 rows × 169 columns
7 Merge everything together
golden_record_df = matched_non_card_holders_w_issue_date_df.merge(
transactions_pivoted_df, on="account_id", how="left"
)
golden_record_df.to_csv("data/golden_record.csv", index=False)
data_reduction["Final Golden Record"] = len(golden_record_df)
assert golden_record_df[
"client_id"
].is_unique, "Each client_id should appear exactly once in the final DataFrame."
assert golden_record_df[
"account_id"
].is_unique, "Each account_id should appear exactly once in the final DataFrame."
golden_record_df.head()| account_id | account_district_id | account_frequency | account_created | account_district_name | account_region | account_inhabitants | account_small_municipalities | account_medium_municipalities | account_large_municipalities | ... | M_4_balance | M_5_balance | M_6_balance | M_7_balance | M_8_balance | M_9_balance | M_10_balance | M_11_balance | M_12_balance | M_13_balance | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 576 | 55 | MONTHLY_ISSUANCE | 1993-01-01 | Brno - venkov | south Moravia | 157042 | 49 | 70 | 18 | ... | 35433.9 | 32763.3 | 30103.6 | 27455.2 | 39623.2 | 41346.8 | 40646.7 | 37953.8 | 35272.1 | 34357.8 |
| 1 | 3818 | 74 | MONTHLY_ISSUANCE | 1993-01-01 | Ostrava - mesto | north Moravia | 323870 | 0 | 0 | 0 | ... | 32448.7 | 20928.3 | 48812.7 | 45898.7 | 38802.0 | 41537.8 | 30863.9 | 49023.8 | 44879.5 | 41579.8 |
| 2 | 704 | 55 | MONTHLY_ISSUANCE | 1993-01-01 | Brno - venkov | south Moravia | 157042 | 49 | 70 | 18 | ... | 49756.6 | 35452.9 | 44789.8 | 33259.4 | 19026.1 | 49039.4 | 31829.9 | 35224.0 | 46092.2 | 31803.3 |
| 3 | 1695 | 76 | MONTHLY_ISSUANCE | 1993-01-03 | Sumperk | north Moravia | 127369 | 31 | 32 | 13 | ... | 98674.9 | 97326.2 | 105257.0 | 68223.8 | 101761.7 | 62686.1 | 55853.1 | 81933.7 | 79168.4 | 99457.9 |
| 4 | 2379 | 44 | MONTHLY_ISSUANCE | 1993-01-10 | Chrudim | east Bohemia | 105606 | 77 | 26 | 7 | ... | 20854.4 | 20774.5 | 19017.2 | 17267.0 | 15524.3 | 19539.7 | 15254.2 | 14955.0 | 13221.3 | 30056.2 |
5 rows × 232 columns
Looking at the first few rows of the final golden record, we can see the aggregated transactional data for each account, with columns representing various metrics for each month leading up to the card issuance date.
plt.figure()
plt.title("Number of Clients by Card Issuance Status")
sns.countplot(x="has_card", data=golden_record_df)
plt.xlabel("Card Issued")
plt.ylabel("Count")
plt.show()We can see that the number of clients with a card issued is equal to the number of clients without a card issued, indicating a successful matching process.
plt.figure()
plt.title("Distribution of Card Issuance Dates")
sns.histplot(
golden_record_df, x="card_issued", hue="has_card", kde=True, bins=30, alpha=0.5
)
plt.xlabel("Card Issuance Date")
plt.ylabel("Count")
plt.show()The distribution of card issuance dates shows that the card issuance process was spread out over time, with an expected identical distribution for clients with and without cards issued.
8 Data Reduction Summary
The following waterfall chart visualizes the data reduction process, highlighting the number of records retained or lost at each stage.
import plotly.graph_objects as go
data_reduction_df = pd.DataFrame(
list(data_reduction.items()), columns=["Category", "Amount"]
)
colors = ["skyblue" if amt >= 0 else "orange" for amt in data_reduction_df["Amount"]]
fig = go.Figure(
go.Waterfall(
name="20",
orientation="v",
measure=["relative"] * (len(data_reduction_df) - 1) + ["total"],
x=data_reduction_df["Category"],
textposition="outside",
text=[f"{amt:,.0f}" for amt in data_reduction_df["Amount"]],
y=data_reduction_df["Amount"],
connector={"line": {"color": "black", "width": 2}},
decreasing={"marker": {"color": "orange"}},
increasing={"marker": {"color": "skyblue"}},
totals={"marker": {"color": "skyblue"}},
)
)
fig.update_layout(
title="Enhanced Data Reduction Waterfall Chart",
xaxis=dict(title="Category"),
yaxis=dict(title="Amount", range=[0, 5500]),
waterfallgap=0.3,
)
fig.show()9 Exploratory Data Analysis: Golden Record
9.1 Comparing Cardholders and Non-Cardholders
9.1.1 Trends in Monthly Financial Metrics
golden_cardholders = golden_record_df[golden_record_df["has_card"]]
golden_non_cardholders = golden_record_df[~golden_record_df["has_card"]]
def plot_trends_with_medians(
cardholders, non_cardholders, columns, title, median_ranges
):
"""
Plots line graphs for average monthly values and annotates medians for specified ranges,
adjusting x-axis indices to match the month sequence from the start.
Parameters:
- cardholders (pd.DataFrame): DataFrame containing data for cardholders.
- non_cardholders (pd.DataFrame): DataFrame containing data for non-cardholders.
- columns (list of str): List of column names ordered by time.
- title (str): Title for the plot.
- median_ranges (list of tuples): Each tuple contains start and end indices for calculating medians.
"""
cardholder_avgs = cardholders[columns].mean()
non_cardholder_avgs = non_cardholders[columns].mean()
months = list(range(1, 1 + len(columns)))
plt.figure()
plt.plot(
months,
cardholder_avgs.values,
marker="o",
linestyle="-",
color="blue",
label="Cardholders",
)
plt.plot(
months,
non_cardholder_avgs.values,
marker="o",
linestyle="-",
color="orange",
label="Non-Cardholders",
)
for start, end in median_ranges:
median_cardholder = cardholders[columns[start : end + 1]].median().median()
median_non_cardholder = (
non_cardholders[columns[start : end + 1]].median().median()
)
plt.hlines(
median_cardholder,
months[start],
months[end],
colors="darkblue",
linestyles="--",
label=f"Median {start+1}-{end+1} (Cardholders): {median_cardholder:.2f}",
)
plt.hlines(
median_non_cardholder,
months[start],
months[end],
colors="red",
linestyles="--",
label=f"Median {start+1}-{end+1} (Non-Cardholders): {median_non_cardholder:.2f}",
)
plt.title(title)
plt.xlabel("Month")
plt.ylabel("Value")
plt.legend()
plt.grid(True)
plt.xticks(months, labels=[f"M_{month}" for month in months]) # Proper month labels
plt.show()9.1.2 Monthly Balance Trends
median_ranges = [
(0, 2),
(9, 11),
] # First 3 months and last 3 months for a 12-month period
balance_columns = [f"M_{i}_balance" for i in range(2, 14)]
plot_trends_with_medians(
golden_cardholders,
golden_non_cardholders,
balance_columns,
"Monthly Balance Trends",
median_ranges,
)9.1.3 Monthly Volume Trends
volume_columns = [
f"M_{i}_volume" for i in range(2, 14)
] # Simulating monthly volume columns
plot_trends_with_medians(
golden_cardholders,
golden_non_cardholders,
volume_columns,
"Monthly Volume Trends",
median_ranges,
)9.1.4 Monthly Transaction Count Trends
transaction_count_columns = [
f"M_{i}_transaction_count" for i in range(2, 14)
] # Simulating monthly transaction count columns
plot_trends_with_medians(
golden_cardholders,
golden_non_cardholders,
transaction_count_columns,
"Monthly Transaction Count Trends",
median_ranges,
)9.1.5 Monthly Positive and Negative Transaction Count Trends
positive_transaction_count_columns = [
f"M_{i}_positive_transaction_count" for i in range(2, 14)
] # Simulating monthly positive transaction count columns
plot_trends_with_medians(
golden_cardholders,
golden_non_cardholders,
positive_transaction_count_columns,
"Monthly Positive Transaction Count Trends",
median_ranges,
)9.1.6 Monthly Negative Transaction Count Trends
negative_transaction_count_columns = [
f"M_{i}_negative_transaction_count" for i in range(2, 14)
] # Simulating monthly negative transaction count columns
plot_trends_with_medians(
golden_cardholders,
golden_non_cardholders,
negative_transaction_count_columns,
"Monthly Negative Transaction Count Trends",
median_ranges,
)9.1.7 Comparison of Average Feature Values
def plot_grouped_comparison(cardholders, non_cardholders, feature_columns):
"""
Plots grouped bar charts for average feature values of cardholders and non-cardholders.
Parameters:
- cardholders (pd.DataFrame): DataFrame containing data for cardholders.
- non_cardholders (pd.DataFrame): DataFrame containing data for non-cardholders.
- feature_columns (list of str): List of column names whose averages to compare.
"""
cardholder_avg = cardholders[feature_columns].mean()
non_cardholder_avg = non_cardholders[feature_columns].mean()
index = range(len(feature_columns))
bar_width = 0.35
fig, ax = plt.subplots()
bars1 = ax.bar(
index, cardholder_avg, bar_width, label="Cardholders", color="skyblue"
)
bars2 = ax.bar(
[p + bar_width for p in index],
non_cardholder_avg,
bar_width,
label="Non-Cardholders",
color="orange",
)
ax.set_xlabel("Feature")
ax.set_ylabel("Average Value")
ax.set_title("Average Feature Values by Group")
ax.set_xticks([p + bar_width / 2 for p in index])
ax.set_xticklabels(feature_columns)
ax.legend()
plt.xticks(rotation=45) # Rotate feature names for better visibility
plt.show()
plot_grouped_comparison(
golden_cardholders,
golden_non_cardholders,
[col for col in golden_record_df.columns if "balance" in col],
)
plot_grouped_comparison(golden_cardholders, golden_non_cardholders, ["loan_amount"])## DEPENDENCIES TODO REMOVE FOR MERGE
# save golden record to temp
golden_record_df.to_parquet("temp/golden_record.parquet")## DEPENDENCY #TODO REMOVE FOR MERGE
import random
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
golden_record_df = pd.read_parquet('temp/golden_record.parquet')
np.random.seed(1337)
random.seed(1337)10 Data Partitioning
The data is split in a 80/20 ratio for training and testing purposes. The stratification ensures that the distribution of the target variable is maintained in both sets. When actually training the models, we will additionally use cross-validation to ensure robust evaluation.
from sklearn.model_selection import train_test_split
class DataModule:
def __init__(self, X_train, X_test, y_train, y_test, feature_columns=None):
self.feature_columns = (
feature_columns if feature_columns is not None else X_train.columns
)
self.X_train = X_train
self.X_test = X_test
self.y_train = y_train
self.y_test = y_test
def create_data_module(df, feature_cols, target_col="has_card", test_size=0.2):
X = df.drop(columns=[target_col])[feature_cols]
y = df[target_col]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=test_size, stratify=y, shuffle=True
)
return DataModule(X_train, X_test, y_train, y_test)
data_module = create_data_module(
golden_record_df, golden_record_df.drop(columns=["has_card"]).columns
)
print(f"Train set size: {len(data_module.X_train)}")
print(f"Test set size: {len(data_module.X_test)}")
print(f"Train set distribution:\n{data_module.y_train.value_counts(normalize=True)}")
print(f"Test set distribution:\n{data_module.y_test.value_counts(normalize=True)}")Train set size: 524
Test set size: 132
Train set distribution:
has_card
False 0.5
True 0.5
Name: proportion, dtype: float64
Test set distribution:
has_card
True 0.5
False 0.5
Name: proportion, dtype: float64
As we can see the distribution of the target variable is maintained in both sets after the split.
11 Model Construction
11.1 Pipeline for Training and Evaluation
The train_evaluate_model function is designed to streamline the process of training and evaluating machine learning models. It performs the following steps:
- Preprocessing: The function automatically handles numerical and categorical features, imputing missing values, scaling numerical features, and one-hot encoding categorical features.
- Model Training: The specified model is trained on the training data.
- Cross-Validation: The model is evaluated using cross-validation with specified evaluation metrics.
- Model Evaluation: The model is evaluated on the test set using various metrics, including accuracy, F1 score, AUC-ROC, precision, and recall.
The pipeline is flexible and can accommodate various models and feature sets, making it a versatile tool for model development and evaluation. It returns a summary of evaluation metrics for both training and test sets, as well as the true labels and predicted probabilities for the test set.
from sklearn.feature_selection import RFECV
import numpy as np
from sklearn.compose import ColumnTransformer
# FIXME validate if it would make more sense to use KNNImputer or iterative imputer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.model_selection import cross_validate, GridSearchCV
from sklearn.metrics import (
make_scorer,
f1_score,
roc_auc_score,
precision_score,
recall_score,
)
import scikitplot as skplt
import dalex as dx
class Trainer:
def __init__(
self,
data_module,
model,
cv=10,
select_features=False,
param_grid=None,
verbose=False,
n_jobs=-1,
):
self.data_module = data_module
self.model = model
self.cv = cv
self.verbose = verbose
self.preprocessor = self._create_preprocessor()
self.select_features = select_features
self.param_grid = param_grid
self.n_jobs = n_jobs
self.pipeline = None
self.train_metrics_report = None
self.test_metrics_report = None
def _create_preprocessor(self):
numerical_features = [
col
for col in self.data_module.X_train.columns
if self.data_module.X_train[col].dtype in ["int64", "float64"]
]
categorical_features = [
col
for col in self.data_module.X_train.columns
if col not in numerical_features
]
other_features = [
col
for col in self.data_module.X_train.columns
if col not in numerical_features + categorical_features
]
if len(other_features) > 0:
raise ValueError(
f"Columns with unsupported data types found: {other_features}"
)
numerical_pipeline = Pipeline(
[("imputer", SimpleImputer(strategy="mean")), ("scaler", StandardScaler())]
)
categorical_pipeline = Pipeline(
[
("imputer", SimpleImputer(strategy="most_frequent")),
("onehot", OneHotEncoder(handle_unknown="ignore")),
]
)
return ColumnTransformer(
transformers=[
("num", numerical_pipeline, numerical_features),
("cat", categorical_pipeline, categorical_features),
]
)
def fit(self):
model_pipeline_steps = [("model", self.model)]
if self.select_features:
model_pipeline_steps.insert(
0,
(
"feature_selection",
RFECV(self.model, verbose=3 if self.verbose else 0, cv=self.cv),
)
)
model_pipeline = Pipeline(model_pipeline_steps)
if self.param_grid is not None:
model_pipeline = GridSearchCV(
model_pipeline,
self.param_grid,
cv=self.cv,
verbose=3 if self.verbose else 0,
n_jobs=self.n_jobs,
)
self.pipeline = Pipeline(
[("preprocessor", self.preprocessor), ("model_pipeline", model_pipeline)]
)
self.pipeline.fit(self.data_module.X_train, self.data_module.y_train)
return self
@staticmethod
def get_scoring_metrics():
return ["accuracy", "f1_macro", "roc_auc", "precision", "recall"]
def eval_train(self):
scoring = {
"accuracy": "accuracy",
"f1_macro": make_scorer(f1_score),
"roc_auc": "roc_auc",
"precision": make_scorer(precision_score),
"recall": make_scorer(recall_score),
}
cv_results = cross_validate(
self.pipeline,
self.data_module.X_train,
self.data_module.y_train,
scoring=scoring,
cv=self.cv,
return_train_score=False,
n_jobs=self.n_jobs,
verbose=3 if self.verbose else 0,
return_estimator=True,
return_indices=True,
error_score="raise",
)
self.train_metrics_report = {
metric: {
"folds": cv_results[f"test_{metric}"].tolist(),
"mean": cv_results[f"test_{metric}"].mean(),
"std": cv_results[f"test_{metric}"].std(),
}
for metric in scoring
}
roc_data = []
for i in range(self.cv):
estimator = cv_results["estimator"][i]
train_indices, test_indices = (
cv_results["indices"]["train"][i],
cv_results["indices"]["test"][i],
)
true_labels = self.data_module.y_train.iloc[test_indices]
y_pred_proba = estimator.predict_proba(
self.data_module.X_train.iloc[test_indices]
)
roc_data.append((true_labels, y_pred_proba))
self.train_metrics_report["roc_data"] = roc_data
return self
def eval_test(self):
X_test, y_test = self.data_module.X_test, self.data_module.y_test
y_pred_proba = (
self.pipeline.predict_proba(X_test)[:, 1]
if hasattr(self.pipeline, "predict_proba")
else np.nan
)
test_metrics = {
"accuracy": self.pipeline.score(X_test, y_test),
"f1_macro": f1_score(
y_test, self.pipeline.predict(X_test), average="macro"
),
"roc_auc": (
roc_auc_score(y_test, y_pred_proba)
if hasattr(self.pipeline, "predict_proba")
else np.nan
),
"precision": precision_score(y_test, self.pipeline.predict(X_test)),
"recall": recall_score(y_test, self.pipeline.predict(X_test)),
}
self.test_metrics_report = {
metric: test_metrics[metric] for metric in test_metrics
}
return self
def get_pipeline(self):
return self.pipeline
def get_preprocessor(self):
return self.preprocessor
def get_train_metrics_report(self):
return self.train_metrics_report
def get_test_metrics_report(self):
return self.test_metrics_report
def get_best_params(self):
if self.param_grid is None:
raise ValueError(
"No hyperparameter grid was provided during model training."
)
best_param = self.pipeline["model_pipeline"].best_params_
return {key.split('__')[1]: value for key, value in best_param.items()}
def get_selected_features(self):
if not self.select_features:
raise ValueError("Feature selection was not enabled during model training.")
if (
self.pipeline is None
or "feature_selection"
not in self.pipeline.named_steps["model_pipeline"].named_steps
):
raise ValueError(
"Feature selection has not been performed or the model is not fitted."
)
rfe = self.pipeline.named_steps["model_pipeline"].named_steps[
"feature_selection"
]
feature_mask = rfe.support_
feature_names = self._get_feature_names_from_preprocessor()
selected_features = [
feature
for feature, is_selected in zip(feature_names, feature_mask)
if is_selected
]
return [
feature
for feature in self.data_module.feature_columns
if any([feature in col for col in selected_features])
]
def _get_feature_names_from_preprocessor(self):
transformers = self.preprocessor.transformers_
feature_names = []
for name, transformer, column in transformers:
if hasattr(transformer, "get_feature_names_out"):
feature_names.extend(transformer.get_feature_names_out(column))
else:
feature_names.extend(column)
return feature_namesThe following class handles the visualization of the model evaluation results. It provides various plots and metrics to assess the model’s performance and interpretability. The class can be used to compare multiple models and visualize their evaluation metrics side by side or individually. There is a distinction made between training and test metrics to ensure a comprehensive evaluation of the model’s performance.
from sklearn.metrics import roc_curve, classification_report, precision_recall_curve
from plotly.subplots import make_subplots
import plotly.express as px
class Visualizer:
def __init__(self, trainer, model_name):
self.trainer = trainer
self.model_name = model_name
X_train, X_test, y_train, y_test = (
self.trainer.data_module.X_train,
self.trainer.data_module.X_test,
self.trainer.data_module.y_train,
self.trainer.data_module.y_test,
)
self.explainer = dx.Explainer(trainer.get_pipeline(), X_test, y_test)
self.X_test = X_test
self.y_true = y_test
self.y_test_pred_proba = trainer.get_pipeline().predict_proba(X_test)
@staticmethod
def compare_evaluation_metrics(visualizers):
model_names = [viz.model_name for viz in visualizers]
metrics = Trainer.get_scoring_metrics()
means = {metric: [] for metric in metrics}
stds = {metric: [] for metric in metrics}
for viz in visualizers:
train_metrics = viz.trainer.get_train_metrics_report()
for metric in metrics:
means[metric].append(np.mean(train_metrics[metric]["folds"]))
stds[metric].append(np.std(train_metrics[metric]["folds"]))
n_groups = len(metrics)
bar_width = 0.15
index = np.arange(n_groups)
opacity = 0.8
plt.figure(figsize=(9, 6))
colors = plt.cm.viridis(np.linspace(0, 1, len(model_names)))
for i, model_name in enumerate(model_names):
bar_positions = index + bar_width * i
bar_values = [means[metric][i] for metric in metrics]
error_values = [stds[metric][i] for metric in metrics]
bars = plt.bar(
bar_positions,
bar_values,
bar_width,
alpha=opacity,
color=colors[i],
yerr=error_values,
capsize=5,
label=model_name,
)
for bar, error in zip(bars, error_values):
yval = bar.get_height()
text_position = yval + error + 0.02
plt.text(
bar.get_x() + bar.get_width() / 2,
text_position,
f"{yval:.2f}",
ha="center",
va="bottom",
fontsize=10,
)
plt.xlabel("Metrics", fontsize=14)
plt.ylabel("Scores", fontsize=14)
plt.title(
f"Cross-Validation (k={visualizers[0].trainer.cv}) Evaluation Metrics Comparison",
fontsize=16,
)
plt.xticks(index + bar_width * (len(model_names) - 1) / 2, metrics, fontsize=12)
plt.ylim(0, 1.1)
plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
plt.grid(True, which="major", linestyle="--", linewidth="0.5", color="grey")
plt.tight_layout()
plt.show()
@staticmethod
def compare_roc_curves(visualizers, dataset):
if dataset not in ["test", "eval"]:
raise ValueError("Invalid dataset option. Choose 'test' or 'eval'.")
plt.figure(figsize=(8, 8))
colors = plt.cm.viridis(np.linspace(0, 1, len(visualizers)))
for i, viz in enumerate(visualizers):
if dataset == "test":
y_true = viz.trainer.data_module.y_test
y_scores = viz.trainer.get_trained_model().predict_proba(
viz.trainer.data_module.X_test
)[:, 1]
elif dataset == "eval":
y_true = []
y_scores = []
for fold in viz.trainer.get_train_metrics_report()["roc_data"]:
y_true.extend(fold[0])
y_scores.extend(fold[1][:, 1])
fpr, tpr, _ = roc_curve(y_true, y_scores)
auc_score = roc_auc_score(y_true, y_scores)
plt.plot(
fpr,
tpr,
label=f"{viz.model_name} (AUC = {auc_score:.2f})",
color=colors[i],
)
plt.plot([0, 1], [0, 1], "k--")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title(f"ROC Curve Comparison on {dataset.capitalize()} Set")
plt.legend(loc="lower right")
plt.show()
def plot_validation_metrics(self):
train_metrics = self.trainer.get_train_metrics_report()
cv = len(train_metrics["accuracy"]["folds"])
metrics = self.trainer.get_scoring_metrics()
fold_scores = {metric: train_metrics[metric]["folds"] for metric in metrics}
plt.boxplot(fold_scores.values(), labels=metrics, notch=True)
plt.title(f"{self.model_name}: Validation Metrics Box Plot (CV={cv})")
plt.xlabel("Metrics")
plt.ylabel("Score")
plt.ylim(0, 1)
plt.grid(True)
plt.show()
def plot_test_metrics(self):
test_metrics = self.trainer.get_test_metrics_report()
test_values = list(test_metrics.values())
test_names = list(test_metrics.keys())
sns.barplot(x=test_names, y=test_values)
plt.title(f"{self.model_name}: Test Metrics")
plt.xlabel("Metrics")
plt.ylabel("Score")
for i, v in enumerate(test_values):
if np.isnan(v):
plt.text(i, 0.5, "N/A", ha="center", va="bottom")
else:
plt.text(i, v + 0.01, f"{v:.2f}", ha="center", va="bottom")
plt.ylim(0, 1)
plt.grid(True)
plt.show()
def plot_confusion_matrix_test(self):
preds = self.y_test_pred_proba.argmax(axis=1)
skplt.metrics.plot_confusion_matrix(self.y_true, preds)
plt.title(f"{self.model_name}: Confusion Matrix")
plt.show()
def plot_classification_report_test(self):
preds = self.y_test_pred_proba.argmax(axis=1)
report = classification_report(self.y_true, preds, output_dict=True)
report_df = pd.DataFrame(report).transpose()
report_df = report_df.round(2)
table = plt.table(
cellText=report_df.values,
colLabels=report_df.columns,
rowLabels=report_df.index,
cellLoc="center",
rowLoc="center",
loc="center",
fontsize=12,
)
table.auto_set_font_size(False)
table.set_fontsize(12)
table.scale(1.2, 1.2)
plt.axis("off")
plt.title(f"{self.model_name}: Classification Report")
plt.show()
def plot_threshold_optimization_test(self):
precision, recall, thresholds = precision_recall_curve(
self.y_true, self.y_test_pred_proba[:, 1]
)
f1_scores = 2 * (precision * recall) / (precision + recall)
optimal_idx = np.argmax(f1_scores)
optimal_threshold = thresholds[optimal_idx]
plt.plot(thresholds, f1_scores[:-1], label="F1-score")
plt.axvline(
x=optimal_threshold,
color="red",
linestyle="--",
label=f"Optimal Threshold: {optimal_threshold:.2f}",
)
plt.title(f"{self.model_name}: Threshold Optimization")
plt.xlabel("Threshold")
plt.ylabel("F1-score")
plt.legend()
plt.show()
def plot_roc_curve_test(self):
skplt.metrics.plot_roc(
self.y_true, self.y_test_pred_proba, plot_micro=False, plot_macro=False
)
plt.title(f"{self.model_name}: ROC Curve on Test Set")
plt.show()
def plot_roc_curve_eval(self, show_folds=False):
fig, ax = plt.subplots(figsize=(8, 8))
colors = plt.cm.viridis(np.linspace(0, 1, self.trainer.cv))
roc_data = self.trainer.get_train_metrics_report()["roc_data"]
for k in range(self.trainer.cv):
true_labels, y_pred_proba = roc_data[k]
fpr, tpr, _ = roc_curve(true_labels, y_pred_proba[:, 1])
auc_score = roc_auc_score(true_labels, y_pred_proba[:, 1])
ax.plot(
fpr, tpr, color=colors[k], label=f"Fold {k + 1} (AUC = {auc_score:.2f})"
)
plt.title(
f"{self.model_name}: ROC Curves for each fold (CV={self.trainer.cv}, "
f'Mean AUROC={self.trainer.train_metrics_report["roc_auc"]["mean"]:.2f})'
)
if show_folds:
plt.legend(loc="lower right")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.grid(True)
plt.show()
def plot_precision_recall_curve_test(self):
skplt.metrics.plot_precision_recall(self.y_true, self.y_test_pred_proba)
plt.title(f"{self.model_name}: Precision-Recall Curve on Test Set")
plt.show()
def plot_lift_curve_test(self):
skplt.metrics.plot_lift_curve(self.y_true, self.y_test_pred_proba)
plt.title(f"{self.model_name}: Lift Curve on Test Set")
plt.show()
def plot_cumulative_gain_curve_test(self):
skplt.metrics.plot_cumulative_gain(self.y_true, self.y_test_pred_proba)
plt.title(f"{self.model_name}: Cumulative Gain Curve on Test Set")
plt.show()
def plot_partial_dependence_test(self, feature):
pdp = self.explainer.model_profile(type="partial", variables=feature)
pdp.plot()
def plot_accumulated_local_effects_test(self, feature):
ale = self.explainer.model_profile(type="accumulated", variables=feature)
ale.plot()
def plot_breakdown_test(self, observation):
breakdown = self.explainer.predict_parts(observation, type="break_down")
breakdown.plot()
def plot_model_explanations_test(self):
feature_importance = self.explainer.model_parts()
feature_importance.plot()
# model_profile = self.explainer.model_profile(type="partial")
# model_profile.plot()
def plot_grid_search(self, log_scale_params):
if self.trainer.param_grid is None:
raise ValueError("No hyperparameter grid was provided during model training.")
cv_results = pd.DataFrame(self.trainer.get_pipeline().named_steps["model_pipeline"].cv_results_)
def shorten_param(param_name):
if "__" in param_name:
return param_name.rsplit("__", 1)[1]
return param_name
cv_results = cv_results.rename(shorten_param, axis=1)
params = {}
for param in log_scale_params:
if cv_results[param].dtype in ["int64", "float64"]:
params[param] = lambda x: np.log10(x) if x > 0 else 0
else:
params[param] = lambda x: x
fig = px.parallel_coordinates(
cv_results.apply(
{
**params,
'mean_test_score': lambda x: x
}
),
color="mean_test_score",
color_continuous_scale=px.colors.sequential.Viridis
)
fig.show()
def visualize_explanations_test(self, feature_columns=[]):
self.plot_model_explanations()
if not feature_columns:
feature_columns = self.trainer.data_module.feature_columns[0]
self.plot_partial_dependence(feature_columns)
self.plot_accumulated_local_effects(feature_columns)
observation = self.trainer.data_module.X_test.iloc[0]
self.plot_breakdown(observation)
plt.show()11.2 Baseline Model: Logistic Regression
baseline_feature_columns = ["age", "client_region"] + [
col
for col in golden_record_df.columns
if "M_" in col and ("_balance" in col or "_volume" in col)
]
baseline_data_module = create_data_module(golden_record_df, baseline_feature_columns)
print(f"Number of baseline feature columns: {len(baseline_feature_columns)}")
print(f"Baseline feature columns: {baseline_feature_columns}")Number of baseline feature columns: 26
Baseline feature columns: ['age', 'client_region', 'M_2_volume', 'M_3_volume', 'M_4_volume', 'M_5_volume', 'M_6_volume', 'M_7_volume', 'M_8_volume', 'M_9_volume', 'M_10_volume', 'M_11_volume', 'M_12_volume', 'M_13_volume', 'M_2_balance', 'M_3_balance', 'M_4_balance', 'M_5_balance', 'M_6_balance', 'M_7_balance', 'M_8_balance', 'M_9_balance', 'M_10_balance', 'M_11_balance', 'M_12_balance', 'M_13_balance']
from sklearn.linear_model import LogisticRegression
baseline_trainer = (
Trainer(baseline_data_module, LogisticRegression(max_iter=10000)).fit().eval_train()
)
baseline_visualizer = Visualizer(baseline_trainer, "Baseline Logistic Regression")
baseline_visualizer.plot_validation_metrics()Preparation of a new explainer is initiated
-> data : 132 rows 26 cols
-> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
-> target variable : 132 values
-> model_class : sklearn.pipeline.Pipeline (default)
-> label : Not specified, model's class short name will be used. (default)
-> predict function : <function yhat_proba_default at 0x7f71b23e5f80> will be used (default)
-> predict function : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
-> predicted values : min = 0.0111, mean = 0.518, max = 1.0
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -0.971, mean = -0.0183, max = 0.803
-> model_info : package sklearn
A new explainer has been created!
baseline_visualizer.plot_confusion_matrix_test()baseline_visualizer.plot_roc_curve_eval(show_folds=True)11.3 Adding more features
In order to possibly improve the model performance, we will include more features in the training data. We will include all features except for the ones that are not relevant for the model training.
After merging the transactional and non-transactional data, we have many columns that are unnecessary for model training. We will remove all columns containing card-related information, except for the has_card column. This decision stems from the fact that 50% of our dataset consists of cardholders and the other 50% consists of non-cardholders, which we matched with the cardholders. Therefore, the data in the non-target card-related columns come from the actual cardholders.
Additionally we will remove all columns that contain time-dependent information, such as dates and IDs, as they are not relevant for the model.
num_cols_before = len(golden_record_df.columns)
golden_record_df = golden_record_df.loc[
:,
~golden_record_df.columns.str.contains("card")
| golden_record_df.columns.str.contains("has_card"),
]
print(
f"Removed {num_cols_before - len(golden_record_df.columns)} card-related columns. Now {len(golden_record_df.columns)} columns remain."
)
num_cols_before = len(golden_record_df.columns)
golden_record_df = golden_record_df.drop(
columns=["loan_granted_date", "birth_date", "account_created"]
)
print(
f"Removed {num_cols_before - len(golden_record_df.columns)} time-dependent columns. Now {len(golden_record_df.columns)} columns remain."
)
num_cols_before = len(golden_record_df.columns)
golden_record_df = golden_record_df.drop(
columns=[
"loan_account_id",
"loan_loan_id",
"order_account_id",
"client_district_name",
"disp_id",
"account_id",
"account_district_name",
]
)
print(
f"Removed {num_cols_before - len(golden_record_df.columns)} ID columns. Now {len(golden_record_df.columns)} columns remain."
)
num_cols_before = len(golden_record_df.columns)
golden_record_df = golden_record_df.drop(
columns=[col for col in golden_record_df.columns if "std" in col]
)
print(
f"Removed {num_cols_before - len(golden_record_df.columns)} std columns. Now {len(golden_record_df.columns)} columns remain."
)
cols_to_exclude_in_train = ["client_id", "has_card"]
all_cols_data_module = create_data_module(
golden_record_df, golden_record_df.drop(columns=cols_to_exclude_in_train).columns
)Removed 6 card-related columns. Now 226 columns remain.
Removed 3 time-dependent columns. Now 223 columns remain.
Removed 7 ID columns. Now 216 columns remain.
Removed 12 std columns. Now 204 columns remain.
11.4 Candidate Models
11.4.1 Logistic Regression
We will train a logistic regression model with the new feature set and evaluate its performance as it already showed promising results in the baseline model.
log_reg_trainer = (
Trainer(all_cols_data_module, LogisticRegression(max_iter=10000)).fit().eval_train()
)
log_reg_visualizer = Visualizer(log_reg_trainer, "Logistic Regression")
log_reg_visualizer.plot_validation_metrics()Preparation of a new explainer is initiated
-> data : 132 rows 202 cols
-> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
-> target variable : 132 values
-> model_class : sklearn.pipeline.Pipeline (default)
-> label : Not specified, model's class short name will be used. (default)
-> predict function : <function yhat_proba_default at 0x7f71b23e5f80> will be used (default)
-> predict function : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
-> predicted values : min = 8.4e-06, mean = 0.479, max = 1.0
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -1.0, mean = 0.0214, max = 0.981
-> model_info : package sklearn
A new explainer has been created!
log_reg_visualizer.plot_confusion_matrix_test()log_reg_visualizer.plot_roc_curve_eval(show_folds=True)11.4.2 Random Forest
We will also train a Random Forest model to see if it can outperform the logistic regression model. Random Forest models are known for their robustness and ability to capture complex relationships in the data.
from sklearn.ensemble import RandomForestClassifier
rf_trainer = (
Trainer(
all_cols_data_module,
RandomForestClassifier(),
)
.fit()
.eval_train()
)
rf_visualizer = Visualizer(rf_trainer, "Random Forest")
rf_visualizer.plot_validation_metrics()Preparation of a new explainer is initiated
-> data : 132 rows 202 cols
-> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
-> target variable : 132 values
-> model_class : sklearn.pipeline.Pipeline (default)
-> label : Not specified, model's class short name will be used. (default)
-> predict function : <function yhat_proba_default at 0x7f71b23e5f80> will be used (default)
-> predict function : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
-> predicted values : min = 0.0, mean = 0.489, max = 0.98
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -0.86, mean = 0.0111, max = 0.74
-> model_info : package sklearn
A new explainer has been created!
rf_visualizer.plot_confusion_matrix_test()rf_visualizer.plot_roc_curve_eval(show_folds=True)11.4.3 Decision Tree
We will also train a Decision Tree model to see how it performs compared to the other models. Decision Trees are known for their interpretability and simplicity.
from sklearn.tree import DecisionTreeClassifier
decision_tree_trainer = (
Trainer(
all_cols_data_module,
DecisionTreeClassifier(),
)
.fit()
.eval_train()
)
decision_tree_visualizer = Visualizer(decision_tree_trainer, "Decision Tree")
decision_tree_visualizer.plot_validation_metrics()Preparation of a new explainer is initiated
-> data : 132 rows 202 cols
-> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
-> target variable : 132 values
-> model_class : sklearn.pipeline.Pipeline (default)
-> label : Not specified, model's class short name will be used. (default)
-> predict function : <function yhat_proba_default at 0x7f71b23e5f80> will be used (default)
-> predict function : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
-> predicted values : min = 0.0, mean = 0.477, max = 1.0
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -1.0, mean = 0.0227, max = 1.0
-> model_info : package sklearn
A new explainer has been created!
decision_tree_visualizer.plot_confusion_matrix_test()decision_tree_visualizer.plot_roc_curve_eval(show_folds=True)11.4.4 Gradient Boosting
Finally, we will train a Gradient Boosting model to see if it can outperform the other models. Gradient Boosting models are known for their high accuracy and ability to capture complex relationships in the data.
from sklearn.ensemble import GradientBoostingClassifier
gradient_boost_trainer = (
Trainer(
all_cols_data_module,
GradientBoostingClassifier(),
)
.fit()
.eval_train()
)
gradient_boost_visualizer = Visualizer(gradient_boost_trainer, "Gradient Boosting")
gradient_boost_visualizer.plot_validation_metrics()Preparation of a new explainer is initiated
-> data : 132 rows 202 cols
-> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
-> target variable : 132 values
-> model_class : sklearn.pipeline.Pipeline (default)
-> label : Not specified, model's class short name will be used. (default)
-> predict function : <function yhat_proba_default at 0x7f71b23e5f80> will be used (default)
-> predict function : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
-> predicted values : min = 0.00355, mean = 0.5, max = 0.985
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -0.985, mean = 0.000161, max = 0.875
-> model_info : package sklearn
A new explainer has been created!
gradient_boost_visualizer.plot_confusion_matrix_test()gradient_boost_visualizer.plot_roc_curve_eval(show_folds=True)12 Model Comparison & Selection
candidate_trainers = [
baseline_trainer,
log_reg_trainer,
rf_trainer,
decision_tree_trainer,
gradient_boost_trainer,
]
candidate_visualizers = [
baseline_visualizer,
log_reg_visualizer,
rf_visualizer,
decision_tree_visualizer,
gradient_boost_visualizer,
]Visualizer.compare_evaluation_metrics(candidate_visualizers)Visualizer.compare_roc_curves(candidate_visualizers, dataset="eval")12.1 Top-N Customer Selection
We will now use the trained models to generate a list of the top N customers who are most likely to get a card (according to the model). We will compare the lists generated by each model to see if there is any overlap in the predictions.
def create_top_n_customers_list(model, data):
mandatory_columns = ["client_id", "has_card"]
if not hasattr(model, "predict_proba"):
raise ValueError("Model does not support probability predictions")
if not all(col in data.columns for col in mandatory_columns):
raise ValueError("Mandatory columns not found in data: 'client_id', 'has_card'")
data = data[data["has_card"] == 0]
probabilities = model.predict_proba(data.copy())
# Probability of having a card (class 1). This essentially gives the clients who should most likely have a card based on the model but don't have one.
probabilities = probabilities[:, 1]
results = pd.DataFrame(
{"Client ID": data["client_id"], "Probability": probabilities}
)
return results.sort_values(by="Probability", ascending=False).reset_index(drop=True)
def compare_top_n_lists(*lists, labels, top_n_percent):
if len(lists) != len(labels):
raise ValueError("Each list must have a corresponding label")
if len(set([len(l) for l in lists])) != 1:
raise ValueError("All lists must have the same length")
for l in lists:
if not l["Probability"].is_monotonic_decreasing:
raise ValueError("Lists must be sorted in descending order of probability")
top_n = int(len(lists[0]) * top_n_percent)
lists = [l.head(top_n) for l in lists]
overlap_matrix = pd.DataFrame(0, index=labels, columns=labels)
for i, list1 in enumerate(lists):
set1 = set(list1["Client ID"])
for j, list2 in enumerate(lists):
set2 = set(list2["Client ID"])
overlap_matrix.iloc[i, j] = len(set1.intersection(set2))
overlap_matrix = overlap_matrix / len(lists[0])
return overlap_matrix
def visualize_overlap_matrix(overlap_matrix, title):
plt.figure(figsize=(10, 8))
mask = np.tril(np.ones_like(overlap_matrix, dtype=bool))
overlap_matrix = overlap_matrix.mask(mask)
sns.heatmap(
overlap_matrix,
annot=True,
cmap="Blues",
cbar_kws={"label": "Common Customers [%]"},
)
plt.title(title)
plt.ylabel("List from Model/Method")
plt.xlabel("List from Model/Method")
plt.xticks(
ticks=np.arange(len(overlap_matrix.columns)) + 0.5,
labels=overlap_matrix.columns,
rotation=45,
ha="right",
)
plt.yticks(
ticks=np.arange(len(overlap_matrix.index)) + 0.5,
labels=overlap_matrix.index,
rotation=0,
)
plt.show()12.2 Selected Model: Random Forest
best_model_trainer = rf_trainer
best_model_visualizer = rf_visualizer
best_model_trainer.eval_test()<__main__.Trainer at 0x7f71f05e9590>
best_model_visualizer.plot_test_metrics()best_model_visualizer.plot_roc_curve_test()_, _ = (
best_model_visualizer.plot_confusion_matrix_test(),
best_model_visualizer.plot_classification_report_test(),
)13 Model Optimization
After selecting the best model, we can further optimize its hyperparameters to improve its performance. We will use grid search with cross-validation to find the best hyperparameters for the logistic regression model.
gs_param_grid = {
"model__n_estimators": [50, 100, 200],
"model__max_depth": [5, 10, 20],
"model__min_samples_split": [2, 5, 10],
"model__min_samples_leaf": [1, 2, 4],
}
gs_trainer = (
Trainer(
all_cols_data_module,
RandomForestClassifier(),
param_grid=gs_param_grid,
verbose=True,
)
.fit()
.eval_train()
)
gs_trainer.get_best_params()Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time= 0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.887 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.906 total time= 0.6s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.731 total time= 0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.885 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.808 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time= 0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.885 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.811 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.769 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.788 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.769 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.885 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.769 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.769 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.885 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.788 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.769 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time= 0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.887 total time= 0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.769 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.769 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.887 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.885 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.788 total time= 0.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.811 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.906 total time= 0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.731 total time= 0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.811 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.811 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.827 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time= 0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time= 0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.788 total time= 0.6s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.811 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.887 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.788 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.887 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.788 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time= 0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.774 total time= 0.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.731 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.906 total time= 0.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time= 0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.750 total time= 0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time= 0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.769 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.887 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.788 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.885 total time= 0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.731 total time= 0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.887 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.731 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.808 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.885 total time= 0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time= 0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time= 0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time= 0.4s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.811 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time= 0.7s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.769 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.769 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.769 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.808 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time= 0.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time= 0.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time= 0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.887 total time= 0.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.885 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.811 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.885 total time= 0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.788 total time= 0.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.769 total time= 0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time= 0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.769 total time= 0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time= 0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.885 total time= 0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.712 total time= 0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.788 total time= 0.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.811 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.811 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.906 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.811 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.769 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.887 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.887 total time= 0.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.731 total time= 0.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.811 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.906 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.885 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.904 total time= 0.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.811 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.808 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time= 0.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.887 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.885 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time= 0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.865 total time= 0.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time= 0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.811 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.7s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time= 0.7s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time= 0.7s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.887 total time= 0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time= 0.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time= 0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time= 0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time= 0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.887 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.788 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.808 total time= 0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.769 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.906 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time= 0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.887 total time= 0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.731 total time= 0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time= 0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time= 0.7s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.885 total time= 0.7s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.769 total time= 0.7s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time= 0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.811 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.887 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.885 total time= 0.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.769 total time= 0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time= 0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.906 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.788 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time= 0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.811 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.731 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.811 total time= 0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.811 total time= 0.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time= 0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.788 total time= 0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.731 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.885 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.769 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.887 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time= 0.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.887 total time= 0.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.731 total time= 0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.769 total time= 0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.769 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.769 total time= 0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.885 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.808 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.811 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.769 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.788 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time= 0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.904 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.769 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.887 total time= 0.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.750 total time= 0.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time= 0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.887 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time= 0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time= 0.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time= 0.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.769 total time= 0.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.788 total time= 0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.887 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.885 total time= 0.6s
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time= 0.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time= 0.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.769 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.887 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.827 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.808 total time= 0.4s
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.7s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.915 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.723 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.771 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.936 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.723 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.936 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.729 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.915 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.702 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.915 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.915 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.771 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.723 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.936 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time= 0.7s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.7s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.915 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.771 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.792 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.915 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time= 1.0s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.723 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV] END accuracy: (test=0.830) f1_macro: (test=0.830) precision: (test=0.815) recall: (test=0.846) roc_auc: (test=0.899) total time= 4.3min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV] END accuracy: (test=0.849) f1_macro: (test=0.846) precision: (test=0.846) recall: (test=0.846) roc_auc: (test=0.889) total time= 4.3min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.723 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.792 total time= 1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.896 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.917 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 0.9s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.896 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.792 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.7s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.723 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.4s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.4s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.4s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.896 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.723 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.729 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.702 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.915 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV] END accuracy: (test=0.830) f1_macro: (test=0.830) precision: (test=0.846) recall: (test=0.815) roc_auc: (test=0.910) total time= 4.3min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 0.9s
[CV] END accuracy: (test=0.849) f1_macro: (test=0.871) precision: (test=0.771) recall: (test=1.000) roc_auc: (test=0.866) total time= 4.3min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.896 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.896 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.896 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.723 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.7s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.896 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.896 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.771 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.896 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.729 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.936 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.915 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.896 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.7s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.896 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.896 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV] END accuracy: (test=0.808) f1_macro: (test=0.833) precision: (test=0.735) recall: (test=0.962) roc_auc: (test=0.910) total time= 4.3min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV] END accuracy: (test=0.769) f1_macro: (test=0.806) precision: (test=0.694) recall: (test=0.962) roc_auc: (test=0.845) total time= 4.3min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.896 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.896 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.896 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.896 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.896 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.896 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 0.9s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.792 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.896 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.896 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.771 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.896 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.723 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.771 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.792 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.4s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.4s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.771 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.4s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.896 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.792 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV] END accuracy: (test=0.808) f1_macro: (test=0.828) precision: (test=0.750) recall: (test=0.923) roc_auc: (test=0.928) total time= 4.3min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.936 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.936 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 0.9s
[CV] END accuracy: (test=0.827) f1_macro: (test=0.830) precision: (test=0.815) recall: (test=0.846) roc_auc: (test=0.898) total time= 4.3min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.896 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.936 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.936 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.936 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.896 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.936 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.936 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.915 total time= 1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.915 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.915 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.771 total time= 0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time= 0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.915 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.936 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.936 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.723 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.936 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.957 total time= 1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.936 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.723 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.936 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.936 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.0s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.957 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.0s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.936 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.915 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.0s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.0s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.7s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.4s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.7s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.915 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.896 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.957 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.936 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.957 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.896 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.915 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time= 1.1s
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=-1)]: Done 10 out of 10 | elapsed: 21.7min finished
{'max_depth': 10,
'min_samples_leaf': 4,
'min_samples_split': 2,
'n_estimators': 100}
gs_visualizer = Visualizer(gs_trainer, "Random Forest Grid Search")
gs_visualizer.plot_grid_search(log_scale_params=["n_estimators", "max_depth", "min_samples_split", "min_samples_leaf"])Preparation of a new explainer is initiated
-> data : 132 rows 202 cols
-> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
-> target variable : 132 values
-> model_class : sklearn.model_selection._search.GridSearchCV (default)
-> label : Not specified, model's class short name will be used. (default)
-> predict function : <function yhat_proba_default at 0x7f71b23e5f80> will be used (default)
-> predict function : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
-> predicted values : min = 0.0, mean = 0.495, max = 0.967
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -0.837, mean = 0.00454, max = 0.698
-> model_info : package sklearn
A new explainer has been created!
gs_visualizer.plot_validation_metrics()gs_visualizer.plot_roc_curve_eval(show_folds=True)gs_visualizer.plot_lift_curve_test()gs_trainer.eval_test()
gs_visualizer.plot_test_metrics()_, _ = (
gs_visualizer.plot_confusion_matrix_test(),
gs_visualizer.plot_classification_report_test()
)Visualizer.compare_evaluation_metrics([best_model_visualizer, gs_visualizer])14 Model Explanation & Reduction
reduced_best_model_trainer = (
Trainer(
all_cols_data_module,
RandomForestClassifier(**gs_trainer.get_best_params()),
select_features=True,
)
.fit()
.eval_train()
)
reduced_best_model_trainer.get_selected_features()[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 0.9s
[CV] END accuracy: (test=0.846) f1_macro: (test=0.852) precision: (test=0.821) recall: (test=0.885) roc_auc: (test=0.919) total time= 4.3min
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time= 1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time= 0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time= 0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time= 0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time= 0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time= 0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time= 0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time= 0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time= 0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time= 0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time= 0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time= 0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time= 0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time= 0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time= 0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time= 1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time= 1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time= 1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time= 1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time= 1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time= 1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time= 0.9s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.915 total time= 0.7s
[CV] END accuracy: (test=0.788) f1_macro: (test=0.820) precision: (test=0.714) recall: (test=0.962) roc_auc: (test=0.893) total time= 4.3min
['age',
'client_unemployment_rate_1996',
'client_tenure_years_relative',
'M_2_volume',
'M_3_volume',
'M_5_volume',
'M_11_volume',
'M_12_volume',
'M_2_total_abs_amount',
'M_10_total_abs_amount',
'M_3_average_amount',
'M_4_average_amount',
'M_5_average_amount',
'M_6_average_amount',
'M_12_average_amount',
'M_4_median_amount',
'M_11_median_amount',
'M_2_min_amount',
'M_3_min_amount',
'M_4_min_amount',
'M_5_min_amount',
'M_12_min_amount',
'M_2_max_amount',
'M_3_max_amount',
'M_4_max_amount',
'M_8_max_amount',
'M_9_max_amount',
'M_10_max_amount',
'M_2_balance',
'M_3_balance',
'M_4_balance',
'M_5_balance',
'M_6_balance',
'M_7_balance',
'M_8_balance',
'M_9_balance',
'M_10_balance',
'M_11_balance',
'M_12_balance',
'M_13_balance']
reduced_best_model_visualizer = Visualizer(
reduced_best_model_trainer, "Reduced Random Forest"
)
reduced_best_model_visualizer.plot_validation_metrics()Preparation of a new explainer is initiated
-> data : 132 rows 202 cols
-> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
-> target variable : 132 values
-> model_class : sklearn.pipeline.Pipeline (default)
-> label : Not specified, model's class short name will be used. (default)
-> predict function : <function yhat_proba_default at 0x7f71b23e5f80> will be used (default)
-> predict function : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
-> predicted values : min = 0.0, mean = 0.49, max = 0.977
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -0.875, mean = 0.00987, max = 0.713
-> model_info : package sklearn
A new explainer has been created!
Visualizer.compare_evaluation_metrics(
[best_model_visualizer, gs_visualizer, reduced_best_model_visualizer]
)reduced_best_model_visualizer.plot_confusion_matrix_test()# reduced_best_model_visualizer.plot_grid_search(log_scale_params=["n_estimators", "max_depth", "min_samples_split", "min_samples_leaf"])reduced_best_model_visualizer.plot_model_explanations_test()14.1 Lift Curve
reduced_best_model_visualizer.plot_lift_curve_test()14.1.1 Top-10% Customer Selection
We will select the top 10% of customers who are most likely to get a card according to each model.
candidate_trainers.append(reduced_best_model_trainer)
customer_lists = [
create_top_n_customers_list(trainer.get_pipeline(), golden_record_df)
for trainer in candidate_trainers
]
candidate_labels = [
"Baseline",
"Logistic Regression",
"Random Forest",
"Decision Tree",
"Gradient Boosting",
"Random Forest (Optimized)"
]top_10_overlap_matrix = compare_top_n_lists(
*customer_lists, labels=candidate_labels, top_n_percent=0.1
)
visualize_overlap_matrix(
top_10_overlap_matrix, "Overlap of Top-10% Customer Lists by Model"
)14.1.2 Top-5% Customer Selection
We will select the top 5% of customers who are most likely to get a card according to each model.
top_5_overlap_matrix = compare_top_n_lists(
*customer_lists, labels=candidate_labels, top_n_percent=0.05
)
visualize_overlap_matrix(
top_5_overlap_matrix, "Overlap of Top-5% Customer Lists by Model"
)